About Scale AI Scale's rapidly growing Global Public Sector team is focused on using AI to address critical challenges facing the public sector around the world. Our core work consists of: - Creating custom AI applications that will impact millions of citizens - Generating high-quality training data for national LLMs - Upskilling and advisory services to spread the impact of AI As these applications move from prototype to production, we're investing heavily in the infrastructure and people that keep them reliable, secure, and trusted by our government partners. At Scale, we're not just building AI solutions - we're enabling the public sector to transform their operations and better serve citizens through cutting-edge technology. If you're ready to shape the future of AI in the public sector and be a founding member of our team, we'd love to hear from you Role Overview We are looking for a highly technical and strategic Senior Manager, Defense Engineering to lead our engineering efforts across the Middle East region, specifically supporting defense-related projects. This senior leader will drive delivery of custom AI applications for defense and national security clients, bridging deep domain knowledge in the defense space with cutting-edge AI and full stack engineering expertise. What You'll Do - Manage and grow the engineering team in the region, driving technical delivery across defense and public sector engagements - Design, build, and optimize backend services for advanced AI-driven applications, with a strong focus on AI agents, evaluation tooling, and automation - Lead full stack engineering efforts end-to-end—from system design and architecture through to debugging, testing, and production deployment - Serve as the senior technical authority on defense-related projects, applying domain knowledge to ensure solutions meet mission-critical standards and security requirements - Architect and deploy AI agent frameworks and API integrations tailored to defense and government workflows - Work comfortably cross-functionally across internal engineering teams and external defense and government clients - Own the full product lifecycle from conceptualization through production, ensuring high-velocity delivery without sacrificing quality - Deliver experiments and MVPs at a high velocity and level of quality to drive engagement with defense customers - Influence the culture, values, and processes of a growing engineering team in the region - Inspire and mentor engineers, fostering a technically excellent and mission-driven team environment - Collaborate with cross-functional teams to define, design, and ship new product features and experiences tailored to defense requirements What We're Looking For - 10+ years of relevant software engineering experience, with a strong emphasis on backend systems and full stack development - 5+ years of experience managing and leading engineering teams, with a proven track record of developing high-performing teams in complex, mission-driven environments - Demonstrated domain knowledge in the defense sector—understanding of defense operations, national security priorities, and the regulatory and security requirements unique to defense - Deep expertise in AI and machine learning, specifically with AI agents, LLM-powered applications, and agentic frameworks - Hands-on experience designing, building, and integrating APIs in production environments (cloud and on-premises), including experience with AI/ML APIs and third-party defense or government platform integrations - Full stack engineering proficiency—comfortable across the entire stack including backend services, APIs, data pipelines, and frontend delivery - Proven success leading, managing, and developing high-performing Engineering teams at scale - Expertise in identifying product engagement patterns and trends for large-scale applications, ideally in government or defense contexts - Track record of shipping high-quality products and features at scale in fast-paced, high-stakes environments - Ability to turn complex defense business and mission requirements into pragmatic, scalable engineering solutions - Excellent problem-solving skills, and ability to work independently and as part of a cross-functional, international team - Experience working in or with the Gulf Cooperation Council (GCC) region is strongly preferred; Arabic language skills a plus PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. About Us: At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status. We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information. We comply with the United States Department of Labor's Pay Transparency provision . PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our privacy policy for additional information.

P-1504 The Applied AI team at Databricks sits at the forefront of advancing GenAI-powered products. Over the past years, we’ve launched Databricks Assistant , AI/BI Genie , and Agent Bricks working with product teams, and made significant strides in LLM quality for these products. These products are used by 100s of thousands of Databricks users every day. We are tackling challenging problems like code suggestion, error detection and correction, text-to-sql generation, automatic pipeline generation, knowledge QA and many others. As our GenAI products continue to evolve, we are seeking multiple GenAI Engineers from junior levels to more senior levels to drive the next phase of development. In 2025, we will focus on enhancing LLM quality, expanding GenAI capabilities across Databricks products, and strengthening our platform architecture to enable seamless AI interactions at scale. Key Responsibilities Shape the direction of our applied AI areas and intelligence features in our products . Drive the development and deployment of state-of-the-art AI models and systems that directly impact the capabilities and performance of Databricks' products and services (e.g., Databricks Assistant and AI/BI Genie). Develop novel data collection, fine-tuning, and LLM technologies that achieve optimal performance on specific tasks and domains. Design and implement ML pipelines for data preprocessing, feature engineering, model training, hyperparameter tuning, and model evaluation, enabling rapid experimentation and iteration. Work closely with cross-functional teams, including AI researchers, ML engineers, and product teams, to deliver impactful AI solutions that enhance user productivity and satisfaction. Build scalable, reusable backend systems to support GenAI products across the company. Develop robust logging, telemetry, and evaluation harnesses to ensure reliable model performance. What We’re Looking For 2-8 years of machine learning engineering experience in high-velocity, high-growth companies. Alternatively, a strong background in relevant ML research in academia will be considered as an equivalent qualification. Strong track record of working with language modeling technologies. This could include the following: Developing generative and embedding techniques, modern model architectures, fine tuning / pre-training datasets, and evaluation benchmarks. Proficiency in Python, TensorFlow/PyTorch, and scalable ML architectures. Ability to drive end-to-end model development, from research and prototyping to deployment and monitoring. Strong analytical and problem-solving skills, with a passion for improving AI-driven user experiences. Strong coding and software engineering skills, and familiarity with software engineering principles around testing, code reviews and deployment. Experience with LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) is a bonus. Why Join Us? At Databricks, we are building state-of-the-art AI solutions that redefine how users interact with data and our products. You’ll have the opportunity to shape the future of AI-driven products at Databricks, work with cutting-edge models, and collaborate with a world-class team of AI and ML experts. If you're excited about pushing the boundaries of AI in real-world applications, we’d love to hear from you! Please note we are open to employees working from our Mountain View, CA office for this position. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . Local Pay Range $190,000 - $285,000 USD About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter , LinkedIn and Facebook . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

Our Mission Our mission is to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and the disabilities that can occur throughout life. For more information, see our website at altoslabs.com. Our Value Our Single Altos Value: Everyone Owns Achieving Our Inspiring Mission . Diversity at Altos Altos Labs has been named one of the Top 3 Biotech Companies and ranked for the second year on the Forbes 2026 Best Startups in America list. At Altos, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining a diverse and inclusive environment. What You Will Contribute To Altos As part of our team, you will help to accelerate and optimize our progress in developing unified, multi-modal generative foundation models for multiscale biology. You will be an integral part of our multidisciplinary teams building the computational platforms that will enable Altos to achieve its mission. In this role, you will partner and collaborate with other multidisciplinary Scientists and Engineers across the Institute of Computation to design, build, and scale state-of-the-art foundation models that tackle biological questions and aid in the discovery of novel interventions for aging and disease. You will focus on the synthesis of unstructured multimodal signals with the structured relational data and knowledge graphs that represent biological reality. The successful candidate will thrive in a fast-paced environment that stresses teamwork, transparency, scientific excellence, originality, and integrity. Responsibilities As a Staff Machine Learning Scientist, you will use your experience to focus on designing, developing, and evaluating state-of-the-art foundation models, at scale, to benefit the research. Pre-train and fine-tune large-scale machine learning systems using multimodal biological data, natural language, and structured relational inputs. Architect and implement novel hybrid models that integrate Large Language Models (LLMs) with Graph Neural Networks (GNNs) for multi-hop reasoning over biological knowledge graphs . Develop Relational Foundation Models (RFMs) that enable zero-shot predictive tasks over heterogeneous, multi-table biological datasets. Lead the design of efficient data loading strategies and distributed training recipes (e.g., FSDP, DeepSpeed) to train models across multiple GPU nodes. Gain insights into model performance based on theory, deep research, and the mathematical underpinnings of set-invariant and graph-structured architectures . Apply strong coding experience to model development and deployment, ensuring research prototypes transition into reliable, scalable production systems. Stay up-to-date on the latest developments in deep learning—including native early-fusion and Mixture-of-Experts (MoE) architectures—and apply this knowledge to Altos' research . Mentor junior staff while maintaining a high individual technical contribution to the core research ecosystem and peer-reviewed publications. Who You Are We are looking for someone who is: Excited about the Altos mission of restoring cell health and resilience to reverse disease, injury, and age-related disabilities. Highly collaborative in mindset and ways of working across research and engineering boundaries. Self-motivated to drive and deliver on long-term technical projects and scientific goals. Demonstrates the desire to grow professionally and expand their skillset in biology, machine learning, and/or drug development. Able to communicate and explain the design, results, and impact of complex AI architectures to both scientific and non-scientific staff. Keen to contribute to seminars and scientific initiatives within Altos and the broader AI research community. Minimum Qualifications PhD in Computer Science, Machine Learning, or a similar quantitative field with 5+ years of relevant work experience in academic or industry settings. Prior experience in developing and implementing novel generative AI models, specifically in multimodal integration, GraphRAG, or relational deep learning . Deep understanding of Machine Learning principles and how they apply to diverse architectures like Transformers, GNNs, and diffusion models . Very strong programming skills in Python and deep learning libraries (e.g., PyTorch, JAX, Hugging Face Transformers/Accelerate). Proven experience with multi-GPU and distributed training at scale (e.g., DDP, FSDP, DeepSpeed, Megatron, or Ray). Strong track record of published, peer-reviewed innovative AI/ML research at top-tier conferences (NeurIPS, ICML, ICLR, CVPR). Preferred Qualifications Familiarity with tabular foundation models (e.g., TabPFN) and in-context learning strategies for structured data . Specific experience in native multimodal modeling (early-fusion) or the synthesis of LLMs and Knowledge Graphs . Track record of ML applied to biological data, such as NGS data (RNA-seq, ATAC-seq), biological imaging (microscopy, IF), or spatial transcriptomics. Experience in optimizing large-scale inference via quantization, distillation, or memory-efficient attention mechanisms. The salary range for Redwood City, CA : Scientist I, Machine Learning: $200,900 - $257,500 Scientist II, Machine Learning: $226,200 - $290,000 Senior Scientist I, Machine Learning: $257,400 - $330,000 The salary range for San Diego, CA : Scientist I, Machine Learning: $179,400 - $230,000 Scientist II, Machine Learning: $212,900 - $273,000 Senior Scientist I, Machine Learning: $239,500 - $307,000 Exact compensation may vary based on skills, experience, and location. LI-NN1 For UK applicants, before submitting your application: - Please click here to read the Altos Labs EU and UK Applicant Privacy Notice ( bit.ly/eu_uk_privacy_notice ) - This Privacy Notice is not a contract, express or implied and it does not set terms or conditions of employment. Equal Opportunity Employment We value collaboration and scientific excellence. We believe that a culture of belonging are foundational to scientific innovation and inquiry. At Altos Labs, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining an inclusive environment. Altos Labs provides equal employment opportunities to all employees and applicants for employment, without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Altos prohibits unlawful discrimination and harassment. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. Thank you for your interest in Altos Labs where we strive for a culture of scientific excellence, learning, and belonging. Note: Altos Labs will not ask you to download a messaging app for an interview or outlay your own money to get started as an employee. If this sounds like your interaction with people claiming to be with Altos, it is not legitimate and has nothing to do with Altos. Learn more about a common job scam at https://www.linkedin.com/pulse/how-spot-avoid-online-job-scams-biron-clark/

The climate crisis is the defining challenge of our time—but it’s also the greatest opportunity for innovation, and a challenge we’re proud to take on. At Overstory, we’re harnessing cutting-edge technology to enable a resilient electrical grid that keeps communities thriving as our world changes. The grid is the backbone of life as we know it. It powers hospitals, keeps food fresh, and ensures communities stay connected. But extreme weather, aging infrastructure, and growing wildfire risks are putting this critical system under pressure. All of this combined makes the electric utility industry the greatest opportunity for tackling climate change. One of the leading causes of catastrophic wildfires and power outages? Trees and brush coming into contact with power lines. That’s where we help. At Overstory, we use AI and advanced satellite imagery to pinpoint and prioritize vegetation risks before they materialize. By giving utilities critical analysis on those risks, we’re helping prevent outages, reduce wildfire risks, and accelerate the transition to a safer, more resilient grid. Our team spans the Americas and Europe, and we work with utility partners across the Americas and beyond. We’re outdoor enthusiasts, musicians, artists, athletes, parents, and adventurers. What unites us is a passion for solving complex problems, a commitment to climate action, and the belief that technology should be a force for good. Join us to help us build a more resilient world together. Role & Team As a Staff Machine Learning Engineer at Overstory, you will lead the development and scaling of our Wildfire Fuel Detection Model. This core engine powers how we understand vegetation structure, fuel loads, and wildfire risk from satellite and environmental data. You’ll help shape the next generation of Overstory’s modeling capabilities by combining cutting-edge ML techniques, large-scale geospatial data, and real-world domain expertise. Reporting to our VP of Product Engineering, you’ll work closely with data scientists, ML engineers, and product teams to ensure our wildfire models are accurate, robust, and production-ready – balancing scientific rigor with practical engineering excellence. As a senior technical leader, you’ll mentor other engineers, drive architectural decisions, and define standards for modeling, experimentation, and deployment across Overstory. Time zone requirement: Eastern North America (NST, AST, EST) What You’ll Do In collaboration with data, ML, and science colleagues, you will: Architect and build advanced ML models to map and predict vegetation and fuel conditions across diverse geographies. Design and maintain robust data and feature pipelines for large-scale geospatial and temporal data. Partner with wildfire science and product teams to define modeling objectives and evaluation metrics tied to real-world impact. Build reproducible experimentation frameworks and model evaluation workflows. Scale models from research to production with a focus on performance, reliability, and explainability. Lead the evolution of ML systems, tooling, and processes — ensuring that our wildfire fuelscape models remain state-of-the-art and maintainable. Collaborate with MLOps peers to streamline training, inference, and monitoring in production environments. Skills & Experience Experience thriving at the intersection of machine learning, geospatial data, and environmental science; deeply motivated by the opportunity to reduce wildfire risk through data-driven insights 10+ years of experience designing and building production-grade ML pipelines and systems Strong background in deep learning, computer vision, or remote sensing Skilled in designing end-to-end ML systems — from data ingestion and preprocessing to deployment and monitoring Hands-on experience with frameworks like PyTorch, TensorFlow, XGBoost, or LightGBM, and data tools like Dask, Spark, or GeoPandas Familiarity with GCP and Vertex AI, or similar cloud-based ML platforms Strong communication skills and ability to collaborate across technical and scientific domains Comfortable leading architectural discussions and mentoring other engineers Nice To Have Background in wildfire science, forestry, or remote sensing Experience integrating physics-based models with ML or working with active learning and uncertainty quantification Experience in model interpretability and data provenance for environmental ML systems Experience with deep learning models for weather or climate data Experience in remote-first or globally distributed teams Note: We believe that all people are capable of great things. We encourage you to apply even if you do not meet all of the requirements that are listed within this job description. What We Offer Competitive, location-specific compensation and benefits Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around Home office stipend, coworking and ongoing education budgets A company culture that genuinely embodies each of our core values To be part of truly mission-driven work that reduces wildfires, protects earth’s natural resources and helps solve our climate crisis About Our Team We are a group of 100 people from all over the world. Fifteen nationalities are represented in our team and at last count we speak fourteen languages: English, Dutch, French, Spanish, German, Italian, Portuguese, Russian, Luxembourgish, Lithuanian, Bulgarian, Cantonese, Estonian, and Danish. We work remotely from eleven countries and are looking for candidates that are living and working in one of them: United States, the Netherlands, United Kingdom, Ireland, Estonia, Portugal, France, Sweden, Switzerland, Denmark and Canada. We gather once a year in-person for our unforgettable team gathering event. We also offer the option to occasionally meet up for in-person collaboration. Diversity & Inclusion The climate crisis is a human crisis that requires diverse perspectives to solve. We place enormous value on diversity and believe that the best ideas emerge when people with different backgrounds and experience work together. We remain committed to scaling a team that reflects the communities we serve, and strive to uphold equitable and inclusive practices across every aspect of our business. We are responsible for creating and maintaining a culture where everyone - regardless of background - has a voice in building a sustainable future. Our Values Tackling the climate crisis is our greatest mission. We act with urgency. Our curiosity fuels our growth. We recognize that change is constant, and we find joy and power in exploration. We’re rooted in diversity. Just as ecosystems need biodiversity to thrive, our resiliency comes from our differences. We care for each other. We love the power of machines but we nurture each other as humans. Trust is fundamental. We assume the best in everyone, and we share ideas openly so that we have a positive impact. _________________________________ Use of AI in Our Hiring Process We sometimes use AI tools to support parts of our hiring process, such as helping us manage applications more efficiently or ensuring job descriptions are clear and inclusive. All hiring decisions are always made by people, not machines. Any data processed by AI is handled securely in line with GDPR and our Privacy Notice .

About Us Valo Health is a human-centric, AI-enabled biotechnology company working to make new drugs for patients faster. The company’s Opal Computational Platform transforms drug discovery and development through a unique combination of real-world data, AI, human translational models and predictive chemistry. Our talented team of biologists, chemists and engineers, armed with advanced AI/ML tools, work together to break down traditional R&D silos and accelerate the speed and scale of drug discovery and development. Valo is committed to hiring diverse talent, prioritizing growth and development, fostering an inclusive environment, and creating opportunities to bring together a group of different experiences, backgrounds, and voices to work together. We embrace new ways of learning, solve complex problems and welcome diverse perspectives that can help us advance patient-centric innovation. Valo is headquartered in Lexington, MA, with additional offices in New York, NY and Tel Aviv, Israel. To learn more, visit  www.valohealth.com . About the Role... As a Staff Data Scientist, Machine Learning in Epidemiology and Patient Data Products, you will be a core member on a team of data scientists building a powerful computational platform for advancing the discovery and development of new medicines. In this role, you will develop machine learning tools for patient data and drive their adoption across teams, under the guidance of epidemiology and biology program leads. Successful candidates will work with a diverse group of scientists and domain experts, in ways that cut across traditional industry boundaries in an innovative startup environment. What You’ll Do… Your primary areas of responsibility will be: As a senior member of our team, you will lead the development of machine learning (ML) methods and analyses of patient data with diverse stakeholders. For example, integrate clinical insights into supervised and unsupervised learning approaches and generate patient profiles. Perform project-specific hands-on analysis and modeling of high-dimensional longitudinal real-world data, spanning electronic medical records (EHRs), clinical notes, sequencing data, and multi-omics, using modern data science tools in cloud environments. Contribute to the design, implementation, and evaluation of innovative machine learning approaches for patient data to provide novel clinical insights. Be comfortable with scientific uncertainty and embrace curiosity and creative solutions. Many of the challenges we tackle don’t have known solutions or established pathways. Use your technical knowledge and intuition to articulate and break down large problems into solvable pieces. There are a lot of problems to solve; you’ll need to prioritize which of these are critical-path today from those that can wait. Be a dynamic and active team member, championing shared coding standards, participating in code reviews, and providing regular updates on your work and input into the work of your colleagues. What You Bring… MS, MPH, or PhD in health data science, biostatistics, or a related quantitative field, with 5 years of experience developing and applying ML methods, including at least 3 years working directly with real-world patient data. Experience in a biopharmaceutical, epidemiological or biostatistical setting is a plus. Extensive experience developing and implementing machine learning solutions in healthcare databases, including EHRs, administrative claims, and patient registries. Familiarity with U.S. and global medical coding ontologies and data models (ICD, ATC, LOINC, SNOMED, CPT, HCPCS, OMOP, etc.). Confident working with highly sparse and high-dimensional data. Experience processing and mining clinical notes is a plus. Extensive experience building, maintaining, and operationalizing ML pipelines, and translating model outputs into meaningful insights for diverse audiences. Broad proficiency across core ML paradigms (e.g., supervised, unsupervised, semi-supervised) and experience with linear and logistic regression, classification and tree‑based methods, clustering and dimensionality‑reduction techniques, and deep learning architectures. Hands-on experience with representation learning and transformer-based and other sequence models is a plus. Strong grounding in key components of the ML development lifecycle, including evaluation metrics, hyperparameter tuning, model selection, feature engineering and selection, model explainability, and MLOps best practices. Mastery of Python and modern data science tools (e.g., scikit-learn, PyTorch, statsmodels, SciPy, MLlib, MLflow). Experience with AI-assisted coding tools (e.g., Claude Code) is a plus. Comfortable working in ambiguous problem spaces; experience working in a start-up or agile work environment as part of cross-functional project teams. Ability to lead and facilitate meetings and work collaboratively on multi-disciplinary project teams. Exceptional time management, ability to prioritize multiple tasks simultaneously, and deliver products on time every time. Enthusiastic about documentation–ensuring that all analyses are clear and reproducible with thorough documentation of key assumptions and decision points. You May Also Bring… Advanced knowledge of biostatistics approaches, including inferential and predictive modeling. Experience in causal approaches for observational studies, including propensity score methods, bias adjustment, and covariate selection and adjustment. Familiarity with or exposure to traditional drug discovery and development processes and approaches. Remote Salary Range $165,000 - $190,000 USD CA Salary Range $175,000 - $220,000 USD Compensation for the role will depend on a number of factors, including a candidate’s qualifications, skills, competencies, and experience. Valo Health currently offers healthcare coverage, annual incentive program, retirement benefits and a broad range of other benefits. Compensation and benefits information is based on Valo Health's good faith estimate as of the date of publication and may be modified in the future. Please note: At this time, we are only able to consider candidates who currently have permanent US work authorization without the need for immediate or future sponsorship.

About us Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career! The Role We're looking for an exceptional leader to spearhead our new Application Engineering team, a self-sufficient and high-impact group focused on localising and advancing our autonomous driving technology for the US market. This is a unique opportunity to shape Wayve’s AV capabilities in the US from the ground up. As a founding manager, you’ll lead a small but mighty team of engineers working across robotics, machine learning, and systems integration. You'll drive development of autonomy features tailored for US's road infrastructure, cultural driving behaviours, and regulatory landscape, ensuring our AV stack performs safely and effectively in this highly distinctive environment. We’re looking for someone who thrives in self-directed, startup-like conditions, capable of setting a vision, executing fast, and making robust decisions independently — while staying aligned with global engineering efforts. This role requires breadth: strong experience across AV systems, including robotics and autonomy, is essential. If you also bring deep expertise in machine learning, that's a major plus. Key Responsibilities: Build and lead a self-sufficient AV development team in the US, hiring and mentoring top talent across Robotics and ML. Deliver autonomy capabilities tailored to road conditions and driving norms, in close collaboration with central Autonomy teams. Drive full-cycle development: from identifying local autonomy needs, to designing, implementing, testing, and deploying features into production. Ensure the team upholds Wayve’s high engineering standards, while operating with agility and independence. Work closely with OEM partners in the US — representing Wayve’s autonomy team in technical discussions, capturing product requirements, and shaping joint development plans. Establish close working relationships with our product and vehicle operations teams in the US. About you To be successful in this role, you'll bring strong technical expertise, proven leadership skills, and a passion for building robust autonomous systems that can adapt to diverse real-world challenges. Essential A strong background in robotics and autonomy, with experience building and deploying systems that operate in real-world environments. Demonstrated ability to lead and grow high-performing engineering teams, ideally in geographically distributed or independent settings. Comfortable with ambiguity: you can define goals, carve out roadmaps, and deliver high-impact work with minimal supervision. Broad technical fluency: capable of reviewing and guiding work across software engineering, ML, controls, and systems integration. Excellent communication skills: you’re able to clearly convey technical context and strategic vision across cultures and time zones. Strong product sense and stakeholder management skills: you’re comfortable interfacing directly with OEM customers and representing engineering in external-facing conversations. Desirable Prior experience in autonomous vehicles or robotic systems operating at scale. Familiarity with US's road environment, driving behaviour, or AV regulatory landscape. A strong foundation in machine learning and its application to real-time decision-making or perception systems. This role is a full-time role based in Sunnyvale, CA or Detroit (hybrid) and the reasonably estimated salary for this role ranges from $336,400 to $381,600, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We operate core working hours so you can determine the schedule that works best for you and your team. LI-KM1 Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. For more information visit Careers at Wayve. To learn more about what drives us, visit Values at Wayve For US candidates only, please visit E-Verify Notice and Participation and Right to Work DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.

About us Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career! The Role As an ML Engineer within the Application Engineering team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalisation, comfort, and collaboration. You’ll design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production. Responsibilities: Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization. Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment. Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness. Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development. Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems. Collaborate cross-functionally across various teams to ensure integration and iteration velocity. Mentor senior engineers and shape the long-term technical direction across Autonomy. About you: In order to set you up for success as a Machine Learning Engineer at Wayve, we’re looking for the following skills and experience. Essential Extensive and proven track record of shipping deep learning systems to production. Expert in deep learning (esp. sequential models, control, planning, or perception). Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices. Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components. Ability to lead technical initiatives across teams, drive alignment, and mentor engineers. Desirable Prior work in autonomous driving, imitation learning, or trajectory prediction. Familiarity with personalization, human behavior modeling, or driver intent inference. Experience integrating ML systems into production hardware or multi-agent simulation. This role is a full-time role based in Sunnyvale or Detroit (hybrid) and the reasonably estimated salary for this role ranges from $283,500 to $381,600, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience. At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. We operate core working hours so you can determine the schedule that works best for you and your team. LI-KM1 Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. For more information visit Careers at Wayve. To learn more about what drives us, visit Values at Wayve For US candidates only, please visit E-Verify Notice and Participation and Right to Work DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.

Overview Black Canyon Consulting ( BCC ) seeks a AI/Machine Learning Specialist that will lead design and deployment of AI/ML models that advance MC&FP’s mission outcomes while ensuring ethical and compliant use. We attract the best people in the business with our competitive benefits package that includes medical, dental and vision coverage, 401k plan with employer contribution, paid holidays, vacation, and tuition and conference reimbursement. If you enjoy being a part of a high performing, professional service and technology focused organization, please apply today! Duties & Responsibilities: Designs AI/ML prototypes, prompt engineering libraries, and ethical AI frameworks aligned with DoD AI Ethics Principles. Implement data pipelines and model governance controls Collaborate with DoD CIO on AI ethics and RMF AI risk alignment Minimum Qualifications: 8 + years of experience in AI, ML, or data science solutions Bachelor’s in Data Science, CS, or Engineering Experience handling Controlled Unclassified Information (CUI) at minimum Public Trust eligibility must have at least Public Trust or ADP/IT-2 investigations Experience with Python, TensorFlow, or PyTorch Preferred Qualifications: CQA, CMQ/OE, or equivalent certification Experience supporting DoD quality programs Experience handling Controlled Unclassified Information (CUI) at minimum Experience supporting DoD quality programs Benefits and Salary We attract the best people in the business with our competitive benefits package that includes medical, dental and vision coverage, 401k plan with employer contribution, paid holidays, vacation, and tuition reimbursement. We offer a competitive salary commensurate with experience and location. The targeted range for this position is $135,000 - $205,000. If you enjoy being a part of a high performing, professional service and technology focused organization, please apply today!

SeatGeek believes live events are powerful experiences that unite humans. With our technological savvy and fan-first attitude we’re simplifying and modernizing the ticketing industry. SeatGeek is a technology innovator on a mission to disrupt the $300 billion ticketing industry. We have the product, vision, and team to make life better for performers, venues, and fans, and build a generational consumer brand in the process. All we’re missing is you. We are looking for Software Engineers with varying levels of experience to join SeatGeek’s R&D team. What you'll do Run a modern, containerized service-oriented architecture using industry-leading software development practices Ship code to production many times a day Solve complex performance problems, build a many-sided marketplace, empower a data-driven business and scale our software to support our booming business Build performant, beautiful, inclusive user interfaces that delight our users and enhance our brand Evaluate new technologies and improve our software stack to keep our technological edge Leverage cutting-edge AI tools to bring ideas to life faster than ever while upholding SeatGeek’s technical and product standards Work across team and discipline boundaries to develop the best product in our industry What you have Experience building business critical software in a fast-paced environment. We'll be interested in hearing about what you've built and how Experience solving complex technical challenges. SeatGeek engineers create custom solutions to unique ticketing problems, including high traffic onsales, interactive venue maps, inventory tracking, and event matching. We'll be excited to hear about challenging problems you've solved Passion for software craftsmanship and product. You have well-considered opinions about how software should work, and hold yourself and your code to a high standard A product mindset. You think beyond the code, about user experience, business impact, and what makes a great product tick Commitment to your teammates. You enjoy working with a diverse group of people with different experiences and take pride in mentoring and learning from others Our stack You do not need experience with all of these, but we thought you might be curious. What we care about is your experience, skills, and approach to problem solving. Tools can be learned. Languages + Frameworks: Python + FastAPI, Go, C# + .NET Core Datastores: Postgres, MemcachedRedis, Elasticsearch Cloud: AWS, k8s, argo Version control: Gitlab AI Tooling: Cursor, Github Copliot, Claude Code Observability: Datadog Client-side: React+Typescript, Swift, Kotlin Perks Equity stake Discretionary annual bonus Flexible work environment, allowing you to work as many days a week in the office as you’d like or 100% remotely A WFH stipend to support your home office setup Unlimited PTO Up to 16 weeks of fully-paid family leave 401(k) matching Student loan matching program Health, vision, dental, and life insurance Up to $25k towards family building, reproductive health services and Gender-affirming care $500 per year for wellness expenses Subscriptions to Headspace (meditation), Headspace Care (therapy), and One Medical $360 per quarter to spend on tickets to live events Annual subscription to Spotify, Apple Music, or Amazon music The salary range for this role is $145,000 - $209,000 USD. This role is equity eligible. In addition, you may receive a discretionary annual bonus based on individual and company performance. Actual compensation packages within that range are based on a wide array of factors unique to each candidate, including but not limited to skill set, years and depth of experience, certifications, and specific location. SeatGeek is committed to providing equal employment opportunities to all employees and applicants for employment regardless of race, color, religion, creed, age, national origin or ancestry, ethnicity, sex, sexual orientation, gender identity or expression, disability, military or veteran status, or any other category protected by federal, state, or local law. As an equal opportunities employer, we recognize that diversity is a positive attribute and we welcome the differences and benefits that a diverse culture brings. Come join us! To review our candidate privacy notice, click here .

Join Axon and be a Force for Good. At Axon, we’re on a mission to Protect Life. We’re explorers, pursuing society’s most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products, we work better together. We connect with candor and care, seeking out diverse perspectives from our customers, communities and each other. Life at Axon is fast-paced, challenging and meaningful. Here, you’ll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter. Your Impact Are you passionate about AI? Do you love engineering solutions enabling fast, cutting-edge science? Are you eager to contribute to building products impacting the world for the greater good? As a Principal ML Engineer at Dedrone, you will contribute to architecting and implementing the platform used by Dedrone scientists to bring safe, secure, reliable AI features to the field of counter UAS devices. Collaborating closely with scientists and software engineers, you will enable new AI capabilities for Dedrone products. As part of a multidisciplinary team, you will be exposed to a wide range of domains and applications: from Multi-modal Sensor Fusion to Real Time Systems with distributed edge compute. The ideal candidate will have a consistent track record of successfully optimizing models for the edge and deploying AI models at the edge, including on device with limited memory & connectivity, to serve users world wide. They will understand the AI lifecycle and be capable of supporting scientists end-to-end along the development of new machine learning models and AI capabilities, leading to better and more responsible AI solutions. They are willing to be bold and stand up to support the team in enabling drone safety measures across Public Safety, Enterprise and Defense domains. What You’ll Do Location: Hybrid from Seattle, WA Reports to: Director of Artificial Intelligence Architect and develop secure, privacy-preserving, on device solutions to enable the continuous improvement of existing AI models. Collaborate with scientists in architecting and implementing state-of-the-art edge distributed training techniques. Implement on device monitoring solutions used for continuous model improvement Implement innovative model compression solutions to enable AI at the edge. Impact the team by bringing your own expertise and deep knowledge of the state-of-the-art to introduce new techniques leading to tangible impact in terms of model fairness, performance, and platform scalability. What You Bring Bachelor’s Degree in Computer Science, Engineering, Physics, Mathematics or an equivalent highly technical field. 13+ years of software engineering experience and a proven track record of successfully architecting and maintaining large-scale distributed platforms. Experience with AI on chips, on device model deployment and management. Proficiency in python, C++, familiarity with ML frameworks such as TensorFlow, or PyTorch. Advanced knowledge and hands-on experience with on chips development. Excellent problem solving skills and ability to dive into system architecture, design, performance metrics, code, test plans, project plans, deployments and operations Comfort communicating and interacting with scientists, engineers and product managers. Preferred Master’s Degree or PhD in Computer Science, Engineering, Physics, Mathematics or an equivalent highly technical field. Hands-on experience in solving Computer Vision or Natural Language Understanding problems in a business setting. Familiarity with responsible AI, de-biasing, model encryption and de-identification techniques. Benefits that Benefit You Competitive salary and 401k with employer match Discretionary paid time off Paid parental leave for all Medical, Dental, Vision plans Fitness Programs Emotional & Mental Wellness support Learning & Development programs Employee Resource Groups (ERGs) And yes, we have snacks in our offices Benefits listed herein may vary depending on the nature of your employment and the location where you work. Location: This role is based out of our Seattle, WA office and follows a hybrid schedule. We rely on in-person collaboration and ask that team members work onsite Tuesdays through Fridays, with the flexibility to work remotely on Mondays, unless there is an approved workplace accommodation. We believe that connection fuels innovation, and our in-office culture is designed to foster meaningful teamwork, mentorship, and shared success. LI-Hybrid Axon is a total compensation company, meaning compensation is made up of base pay, bonus, and stock awards. The actual base pay is dependent upon many factors, such as: level, function, training, transferable skills, work experience, business needs, geographic market, and often a combination of all these factors. Our benefits offer an array of options to help support you physically, financially and emotionally through the big milestones and in your everyday life. To see more details on our benefits offerings please visit https://www.axon.com/careers . Base Pay Range $177,000 - $283,200 USD Don’t meet every single requirement? That's ok. At Axon, we Aim Far. We think big with a long-term view because we want to reinvent the world to be a safer, better place. We are also committed to building diverse teams that reflect the communities we serve. Studies have shown that women and people of color are less likely to apply to jobs unless they check every box in the job description. If you’re excited about this role and our mission to Protect Life but your experience doesn’t align perfectly with every qualification listed here, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Important Notes The above job description is not intended as, nor should it be construed as, exhaustive of all duties, responsibilities, skills, efforts, or working conditions associated with this job. The job description may change or be supplemented at any time in accordance with business needs and conditions. Some roles may also require legal eligibility to work in a firearms environment. We collect personal information from applicants to evaluate candidates for employment. You may request access, deletion, or exercise other CCPA rights at axongreenhousesupport@axon.com or via our Axon Privacy Web Form . For more information, please see the Your California Privacy Rights section of our Applicant and Candidate Privacy Notice. Axon’s mission is to Protect Life and is committed to the well-being and safety of its employees as well as Axon’s impact on the environment. All Axon employees must be aware of and committed to the appropriate environmental, health, and safety regulations, policies, and procedures. Axon employees are empowered to report safety concerns as they arise and activities potentially impacting the environment. We are an equal opportunity employer that promotes justice, advances equity, values diversity and fosters inclusion. We’re committed to hiring the best talent — regardless of race, creed, color, ancestry, religion, sex (including pregnancy), national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, genetic information, veteran status, or any other characteristic protected by applicable laws, regulations and ordinances — and empowering all of our employees so they can do their best work. If you have a disability or special need that requires assistance or accommodation during the application or the recruiting process, please email recruitingops@axon.com. Please note that this email address is for accommodation purposes only. Axon will not respond to inquiries for other purposes. Phishing alert: Axon will never ask you to pay for any part of the hiring process, including training, equipment, or background checks. We do not make job offers via text message, WhatsApp, or instant messaging platforms without a formal interview process. All legitimate job openings are listed on our official careers page at https://www.axon.com/careers . If you receive a suspicious offer or outreach from an email address that is not @axon.com , or if you are asked for sensitive personal information (bank details, Social Security Number) prematurely, please ignore the message and report it to recruitingops@axon.com .

Join Axon and be a Force for Good. At Axon, we’re on a mission to Protect Life. We’re explorers, pursuing society’s most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products, we work better together. We connect with candor and care, seeking out diverse perspectives from our customers, communities and each other. Life at Axon is fast-paced, challenging and meaningful. Here, you’ll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter. Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI team, focusing on AI applications (LLM and Computer Vision) in Cloud, Devices and Robotics. As a key member of our research and development efforts, you will play a crucial role in advancing the state-of-the-art in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Computer Vision and GenAI technologies for law enforcement and beyond. You will collaborate with cross-functional teams to design, develop, and deploy cutting-edge LLM, MLLM, CV models and algorithms and solutions that enable intelligent reasoning, perception and understanding of multimodal data. What You’ll Do Location: any cities with Axon Engineering Hub in US, Vietnam, EU (see https://www.axon.com/company ) US: Seattle, Boston, Scottsdale Responsibilities Own one or more key technical areas across LLM, MLLM, CV product portfolio. Provide technical leadership to junior scientists, guiding the transition of R&D concepts into impactful Axon product feature. Research and develop cutting-edge techniques in LLM, MLLMs, GenAI, and Computer Vision across cloud, devices and sensors based data sources. Design and implement efficient and scalable MLLM models for inference and analysis of multimodal data. Explore novel approaches to address challenges in NLP, NLU, Object Detection, Object Recognition, Object Tracking, Segmentation, and Scene Understanding. Optimize AI models, algorithms for performance, memory footprint, and energy efficiency to meet the requirements of resource-constrained devices. Join force with MLEs or firmware or hardware engineers to leverage hardware accelerators and optimize algorithms for specific hardware architectures. Evaluate the performance of LLM, MLLM, CV models using real-world datasets and design experiments to validate their effectiveness. Stay up-to-date with the latest research trends and advancements in computer vision, machine learning, and deep learning, MLLMs, GenAI and integrate relevant findings into our projects. Contribute to patent disclosures, academic publications, and technical documentation to share insights and findings with the broader community. Experience coach and mentor junior scientists. What You Bring PhD and with +5 years for ML Scientist, +8 years for Sr. ML Scientist, +10 years for Principal ML Scientist experience in Computer Science or a related field with a focus on LLM, MLLMs, Computer Vision, GenAI. Proven track record of research excellence in LLM, MLLM, Computer Vision, Robotics Perception, GenAI, demonstrated through publications in top-tier conferences or journals. Strong proficiency in programming languages such as Python, C/C++, experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras and experience with ROS or robotic operational system. Drive one or more phases of the ML development lifecycle: shape datasets, investigate modeling approaches and architectures, train/evaluate/tune models and implement the end-to-end training pipeline. Leverage state-of-the-art research to deliver high quality models enabling multiple AI projects at scale. Contribute back to the research community via academic publications, tech blogs, open-source code and contributing to internal/external AI challenges Experience in developing computer vision algorithms for resource-constrained devices such as mobile phones, IoT devices, or embedded systems is highly desirable. Excellent problem-solving skills, analytical thinking, and the ability to work independently as well as collaboratively in a team environment. Strong communication skills and the ability to effectively present complex technical concepts to both technical and non-technical audiences. Benefits that Benefit You Competitive salary and 401k with employer match Discretionary paid time off Paid parental leave for all Medical, Dental, Vision plans Fitness Programs Emotional & Mental Wellness support Learning & Development programs And yes, we have snacks in our offices Benefits listed herein may vary depending on the nature of your employment and the location where you work Location: This role is based out of our Seattle, WA office and follows a hybrid schedule. We rely on in-person collaboration and ask that team members work onsite Tuesday through Friday, with flexibility to work remotely on Mondays. We believe connection fuels innovation, and our in-office culture is designed to support meaningful teamwork and mentorship. Axon is a total compensation company, meaning compensation is made up of base pay, bonus, and stock awards. The actual base pay is dependent upon many factors, such as: level, function, training, transferable skills, work experience, business needs, geographic market, and often a combination of all these factors. Our benefits offer an array of options to help support you physically, financially and emotionally through the big milestones and in your everyday life. To see more details on our benefits offerings please visit https://www.axon.com/careers . Base Pay Range $159,750 - $255,600 USD Don’t meet every single requirement? That's ok. At Axon, we Aim Far. We think big with a long-term view because we want to reinvent the world to be a safer, better place. We are also committed to building diverse teams that reflect the communities we serve. Studies have shown that women and people of color are less likely to apply to jobs unless they check every box in the job description. If you’re excited about this role and our mission to Protect Life but your experience doesn’t align perfectly with every qualification listed here, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Important Notes The above job description is not intended as, nor should it be construed as, exhaustive of all duties, responsibilities, skills, efforts, or working conditions associated with this job. The job description may change or be supplemented at any time in accordance with business needs and conditions. Some roles may also require legal eligibility to work in a firearms environment. We collect personal information from applicants to evaluate candidates for employment. You may request access, deletion, or exercise other CCPA rights at axongreenhousesupport@axon.com or via our Axon Privacy Web Form . For more information, please see the Your California Privacy Rights section of our Applicant and Candidate Privacy Notice. Axon’s mission is to Protect Life and is committed to the well-being and safety of its employees as well as Axon’s impact on the environment. All Axon employees must be aware of and committed to the appropriate environmental, health, and safety regulations, policies, and procedures. Axon employees are empowered to report safety concerns as they arise and activities potentially impacting the environment. We are an equal opportunity employer that promotes justice, advances equity, values diversity and fosters inclusion. We’re committed to hiring the best talent — regardless of race, creed, color, ancestry, religion, sex (including pregnancy), national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, genetic information, veteran status, or any other characteristic protected by applicable laws, regulations and ordinances — and empowering all of our employees so they can do their best work. If you have a disability or special need that requires assistance or accommodation during the application or the recruiting process, please email recruitingops@axon.com. Please note that this email address is for accommodation purposes only. Axon will not respond to inquiries for other purposes. Phishing alert: Axon will never ask you to pay for any part of the hiring process, including training, equipment, or background checks. We do not make job offers via text message, WhatsApp, or instant messaging platforms without a formal interview process. All legitimate job openings are listed on our official careers page at https://www.axon.com/careers . If you receive a suspicious offer or outreach from an email address that is not @axon.com , or if you are asked for sensitive personal information (bank details, Social Security Number) prematurely, please ignore the message and report it to recruitingops@axon.com .

Join Axon and be a Force for Good. At Axon, we’re on a mission to Protect Life. We’re explorers, pursuing society’s most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products, we work better together. We connect with candor and care, seeking out diverse perspectives from our customers, communities and each other. Life at Axon is fast-paced, challenging and meaningful. Here, you’ll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter. Your Impact We are seeking highly skilled and innovative Machine Learning Scientists to join our AI team, focusing on AI applications (LLM and Computer Vision) in Cloud, Devices and Robotics. As a key member of our research and development efforts, you will play a crucial role in advancing the state-of-the-art in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Computer Vision and GenAI technologies for law enforcement and beyond. You will collaborate with cross-functional teams to design, develop, and deploy cutting-edge LLM, MLLM, CV models and algorithms and solutions that enable intelligent reasoning, perception and understanding of multimodal data. What You’ll Do Location: any cities with Axon Engineering Hub in US, Vietnam, EU (see https://www.axon.com/company ) US: Seattle, Boston, Scottsdale Responsibilities Own one or more key technical areas across LLM, MLLM, CV product portfolio. Provide technical leadership to junior scientists, guiding the transition of R&D concepts into impactful Axon product feature. Research and develop cutting-edge techniques in LLM, MLLMs, GenAI, and Computer Vision across cloud, devices and sensors based data sources. Design and implement efficient and scalable MLLM models for inference and analysis of multimodal data. Explore novel approaches to address challenges in NLP, NLU, Object Detection, Object Recognition, Object Tracking, Segmentation, and Scene Understanding. Optimize AI models, algorithms for performance, memory footprint, and energy efficiency to meet the requirements of resource-constrained devices. Join force with MLEs or firmware or hardware engineers to leverage hardware accelerators and optimize algorithms for specific hardware architectures. Evaluate the performance of LLM, MLLM, CV models using real-world datasets and design experiments to validate their effectiveness. Stay up-to-date with the latest research trends and advancements in computer vision, machine learning, and deep learning, MLLMs, GenAI and integrate relevant findings into our projects. Contribute to patent disclosures, academic publications, and technical documentation to share insights and findings with the broader community. Experience coach and mentor junior scientists. What You Bring PhD and with +5 years for ML Scientist, +8 years for Sr. ML Scientist, +10 years for Principal ML Scientist experience in Computer Science or a related field with a focus on LLM, MLLMs, Computer Vision, GenAI. Proven track record of research excellence in LLM, MLLM, Computer Vision, Robotics Perception, GenAI, demonstrated through publications in top-tier conferences or journals. Strong proficiency in programming languages such as Python, C/C++, experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras and experience with ROS or robotic operational system. Drive one or more phases of the ML development lifecycle: shape datasets, investigate modeling approaches and architectures, train/evaluate/tune models and implement the end-to-end training pipeline. Leverage state-of-the-art research to deliver high quality models enabling multiple AI projects at scale. Contribute back to the research community via academic publications, tech blogs, open-source code and contributing to internal/external AI challenges Experience in developing computer vision algorithms for resource-constrained devices such as mobile phones, IoT devices, or embedded systems is highly desirable. Excellent problem-solving skills, analytical thinking, and the ability to work independently as well as collaboratively in a team environment. Strong communication skills and the ability to effectively present complex technical concepts to both technical and non-technical audiences. Benefits that Benefit You Competitive salary and 401k with employer match Discretionary paid time off Paid parental leave for all Medical, Dental, Vision plans Fitness Programs Emotional & Mental Wellness support Learning & Development programs And yes, we have snacks in our offices Benefits listed herein may vary depending on the nature of your employment and the location where you work Location: This role is based out of our Boston, MA office and follows a hybrid schedule. We rely on in-person collaboration and ask that team members work onsite Tuesday through Friday, with flexibility to work remotely on Mondays. We believe connection fuels innovation, and our in-office culture is designed to support meaningful teamwork and mentorship. Axon is a total compensation company, meaning compensation is made up of base pay, bonus, and stock awards. The actual base pay is dependent upon many factors, such as: level, function, training, transferable skills, work experience, business needs, geographic market, and often a combination of all these factors. Our benefits offer an array of options to help support you physically, financially and emotionally through the big milestones and in your everyday life. To see more details on our benefits offerings please visit https://www.axon.com/careers . Base Pay Range $159,750 - $255,600 USD Don’t meet every single requirement? That's ok. At Axon, we Aim Far. We think big with a long-term view because we want to reinvent the world to be a safer, better place. We are also committed to building diverse teams that reflect the communities we serve. Studies have shown that women and people of color are less likely to apply to jobs unless they check every box in the job description. If you’re excited about this role and our mission to Protect Life but your experience doesn’t align perfectly with every qualification listed here, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Important Notes The above job description is not intended as, nor should it be construed as, exhaustive of all duties, responsibilities, skills, efforts, or working conditions associated with this job. The job description may change or be supplemented at any time in accordance with business needs and conditions. Some roles may also require legal eligibility to work in a firearms environment. We collect personal information from applicants to evaluate candidates for employment. You may request access, deletion, or exercise other CCPA rights at axongreenhousesupport@axon.com or via our Axon Privacy Web Form . For more information, please see the Your California Privacy Rights section of our Applicant and Candidate Privacy Notice. Axon’s mission is to Protect Life and is committed to the well-being and safety of its employees as well as Axon’s impact on the environment. All Axon employees must be aware of and committed to the appropriate environmental, health, and safety regulations, policies, and procedures. Axon employees are empowered to report safety concerns as they arise and activities potentially impacting the environment. We are an equal opportunity employer that promotes justice, advances equity, values diversity and fosters inclusion. We’re committed to hiring the best talent — regardless of race, creed, color, ancestry, religion, sex (including pregnancy), national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, genetic information, veteran status, or any other characteristic protected by applicable laws, regulations and ordinances — and empowering all of our employees so they can do their best work. If you have a disability or special need that requires assistance or accommodation during the application or the recruiting process, please email recruitingops@axon.com. Please note that this email address is for accommodation purposes only. Axon will not respond to inquiries for other purposes. Phishing alert: Axon will never ask you to pay for any part of the hiring process, including training, equipment, or background checks. We do not make job offers via text message, WhatsApp, or instant messaging platforms without a formal interview process. All legitimate job openings are listed on our official careers page at https://www.axon.com/careers . If you receive a suspicious offer or outreach from an email address that is not @axon.com , or if you are asked for sensitive personal information (bank details, Social Security Number) prematurely, please ignore the message and report it to recruitingops@axon.com .

Cresta unlocks the true potential of the customer experience, turning every conversation into a competitive advantage. Cresta’s unified AI platform combines conversational AI agents, real-time human agent augmentation, and comprehensive conversation intelligence to drive revenue and efficiency gains across every channel. The world’s leading companies, including United Airlines, Cox Communications, and Marriott, use Cresta to power world-class customer experiences every day. Born from the Stanford AI Lab, Cresta has raised more than $270 million from the world’s leading investors, including a16z, Greylock, and Sequoia. Cresta’s leadership includes some of the leading minds in AI today. Our CEO, Ping Wu , founded and led Google's Contact Center AI and Vertex AI platforms before joining Cresta to build the future of AI-driven customer experiences. Over the next few years, AI is going to redefine how people all over the world interact with businesses every day. Come build that future at Cresta. Cresta is on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. Our platform combines the best of AI and human intelligence to help contact centers discover customer insights and behavioral best practices, automate conversations and inefficient processes, and empower every team member to work smarter and faster. Born from the prestigious Stanford AI lab, Cresta's co-founder and chairman is Sebastian Thrun , the genius behind Google X, Waymo, Udacity, and more. Our leadership also includes CEO, Ping Wu , the co-founder of Google Contact Center AI and Vertex AI platform, and co-founder, Tim Shi , an early member of Open AI. Join us on this thrilling journey to revolutionize the workforce with AI. The future of work is here, and it's at Cresta. About the role: At Cresta, the Knowledge Assist (KA) team develops AI solutions for the contact center industry, focusing on improving agent productivity by providing access to the right knowledge at the right time. Our current projects: Generative Knowledge Assist (GenKA): Real-time, context-aware suggestions for contact center agents, integrating with multiple knowledge bases to streamline information retrieval, reduce agent effort, and ensure accurate responses. Knowledge Search (KS): A search experience tailored for contact center agents. Built on the GenKA stack, it aims to evolve into an enterprise search solution beyond contact centers. Retrieval-Augmented Generation (RAG): A critical project underpinning GenKA, KS, and VA, enabling efficient retrieval of relevant knowledge content and generating read-to-use agent responses grounded in knowledge. Our internships offer a dynamic, fast-paced environment where you’ll collaborate with top researchers and engineers in the field. We provide opportunities for interns to make significant contributions to AI research and apply novel techniques at scale. This is a unique opportunity to shape the future of AI at Cresta by solving complex problems and bringing breakthrough AI advancements into production environments. Responsibilities : Design, develop, and deploy Cresta’s KA solutions and proprietary models. Focus on practical AI challenges such as improving reasoning, and evaluation in real-world scenarios. Collaborate with cross-functional teams including front-end and back-end software engineers to integrate KA solutions into Cresta’s customer solutions. Lead initiatives to scale AI systems for production environments, ensuring performance and reliability across use cases. Contribute to solving cutting-edge problems in AI and help define the future roadmap for Cresta’s KA. Innovate and research ways to improve security, cost-efficiency, and reliability of AI systems. Qualifications We Value: Currently pursuing a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field. Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow). Strong understanding of machine learning fundamentals and generative modeling. Ability to design and analyze experiments involving large-scale datasets. Work authorization in the country of employment at the time of hire. Perks & Benefits: $45-$70 per hour subject to taxes Lunch can be expensed (up to $25) while working in the office. PTO: 4 days Compensation for this position includes a base salary, equity, and a variety of benefits. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable. We are actively hiring for this role in the US and Canada. Your recruiter can provide further details. This posting will be used to fill a newly-created role. We have noticed a rise in recruiting impersonations across the industry, where scammers attempt to access candidates' personal and financial information through fake interviews and offers. All Cresta recruiting email communications will always come from the @cresta.ai domain. Any outreach claiming to be from Cresta via other sources should be ignored. If you are uncertain whether you have been contacted by an official Cresta employee, reach out to recruiting@cresta.ai

Vectara provides a scalable platform to deploy your Enterprise AI Agents and AI Assistants with Accuracy, Security, and Explainability like no other solution. Our enterprise RAG Platform offers unparalleled Accuracy, Security, and Explainability by leveraging the strongest models for retrieval, embedding, reranking , a optimized LLM trained for quality , and advanced Hallucination Mitigation . We are the developers of the Hughes Hallucination Evaluation Model and Correction model, core to ensuring accuracy, quality, and responsible AI that is production ready . These innovations have been cited in the New York Times, Visual Capitalist , and many other leading publications. This platform has allowed us to be very successful with over 100 Enterprise clients including the likes of large US military organizations, Financial services, Healthcare, and Manufacturing. Our founding team includes industry veterans and experts in neural information retrieval and distributed systems from Google. Join us as we pursue our mission to help the world find meaning. People at Vectara are passionate about ensuring customers take advantage of breakthroughs in applied Artificial Intelligence (AI) to solve real-world technology and business problems today. Our team is a group of unquestionable all-stars in their respective fields of computer science and business from Google, Cloudera, Splunk, MongoDB, Elastic, and more. Job responsibilities Design, prototype, research and build AI systems for Vectara. Train, evaluate and deploy ML models in the domains of Natural Language Processing, Information Retrieval, AI Agents, Large Language Models (LLMs) and Multimodal Large Language Model (MMLLMs). Improve the quality of Vectara’s AI Agents and RAG-as-a-service platform, working on features like multilinguality, self-supervised learning, agentic behavior and hallucination reduction. Publish technical blogs, papers, and patents. Requirements: BS/MS in Computer Science, Statistics, Electrical/Computer Engineering, Mathematics, or a related field. 5+/4+ years of professional work experience after BS/MS applying machine learning to real-world problems, and crafting scalable and effective ML/AI solutions. Strong domain knowledge in at least one of the following: RAG, LLM, information retrieval, Multimodal LLMs. Excellent programming skills in Python. Proficiency in data/ML libraries such as pandas, transformers, and torch. Familiarity with the technical details of deep learning concepts, such as Transformers, Retrieval-Augmented Generation (RAG), mixture of experts (MoE). Hands-on experience in training ML systems end-to-end from data curation to evaluation and deployment. Preferred requirements: PhD in Computer Science/Engineering with 1+ years of industry experience. Publications in prestigious venues such as ACL, NAACL, EMNLP, NeurIPS, ICML, ICLR as a key author. Experience as an ML engineer in an early-stage, high growth environment. Expertise in the following areas: Embedding models, rerankers Multimodal retrieval, question answering, and reasoning Vector databases, BM25 Planning and reasoning in LLMs Multilinguality in LLMs NLG Evaluation such as hallucination detection Location requirements: We support remote applicants from all over the US but candidates who can come to the office 2-3 days a week in our Palo Alto office are preferred. Equity and Salary Range: Salary is just one component of Vectara’s employee compensation. Our full-time employees are also equity owners in the company, which although not an immediate cash component, can have positive impacts on long-term total compensation for each participating employee. We would be remiss if we didn’t highlight and celebrate our focus on engaging many of our employees in being economic co-owners of the business. Vectara welcomes all. We value the collective wisdom of people from different backgrounds, experiences, abilities and perspectives. We never discriminate on the basis of race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. Vectara has a positive and supportive culture—we look for people who are inventive and work to be a little better every single day. We seek to be smart, humble, hardworking and, above all, curious. After all, we are on a mission to find meaning. Perks and Benefits: 100% paid Medical, Dental, Vision begins on your first day! Option of Health Savings Account (HSA) or Flexible Savings Account (FSA). Generous paid time off (PTO) plus paid sick time, holidays, and company rest days. Professional development and training opportunities. Company virtual happy hours and fun team building activities and more.

About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. About the Role: Building a great AI assistant is only half the battle – knowing whether it's actually great is the other half. Our team owns the measurement and quality layer that make Glean's Assistant and Agents reliably better over time: evaluation pipelines, quality evalsets, LLM-powered judges, agent observability, and the tooling engineers use to understand what changed and why. It's a rare combination of infrastructure engineering, applied ML, and direct product impact. If you care deeply about quality and want to build the systems that make it measurable, this role is for you. You will: Design and curate evaluation datasets – sampling strategies, query diversity, and golden sets that give reliable, representative coverage of real assistant behavior. Build and maintain large-scale evaluation pipelines that measure assistant quality across thousands of real user queries. Build LLM-powered judges that score metrics like correctness, completeness, and response quality, and align them against human judgment. Evaluate new models and product changes before they ship – providing the quality signal that gates launches and prevents regressions. Build observability infrastructure for AI agents: trace enrichment, data pipelines, and dashboards that make assistant behavior inspectable. Close the loop between quality measurement and improvement using eval results, customer feedback, and techniques like automated prompt iteration to help drive concrete gains in assistant behavior. Collaborate with engineers across the company to make evals a first-class part of how we ship. About you: 2+ years of software engineering experience with strong coding skills. Strong backend fundamentals in Go and Python; comfortable with distributed data pipelines. Experience working with LLM evaluation, reinforcement learning from human feedback, natural language processing, or other large systems involving machine learning. Analytically rigorous – you think carefully about what offline metrics actually predict about real user experience. Thrive in a customer-focused, tight-knit and cross-functional environment - being a team player and willing to take on whatever is most impactful for the company You care about quality – not just in the systems you build, but in the product you're helping measure and improve. Location: This role is hybrid (3-4 days a week in one of our SF Bay Area offices) Compensation & Benefits: The standard base salary range for this position is $200,000 - $300,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused. We are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organization. We're committed to an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race. LI-HYBRID AI-First Mindset at Glean: At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required. Global Data Privacy Notice for Job Candidates and Applicants: Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy . By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement . By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement , and I agree to the terms.

About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. About the Role: Glean is seeking a few Machine Learning engineers who want to focus on a combination of Quality and traditional ML work to help us build the Enterprise Brain. The Enterprise Brain team is developing a suite of proactive AI products that aims to revolutionize enterprise workflows by proactively detecting and automating tasks for users - thus unlocking true productivity. This is built on top of a deep user understanding and state of the art Enterprise graph. The project involves using both LLM and other advanced ML techniques, agent orchestration and cutting-edge ranking techniques. You will: Work on deeply challenging ML problems involving user understanding and task prediction. Invent new LLM workflows and signals to improve reasoning, planning, and personalization. Design and optimize reinforcement learning and fine-tuning approaches to improve the quality of understanding, prediction and other agentic systems. Lead development of scalable evaluation, benchmarking, and optimization loops. Build and maintain robust ML pipelines for enterprise and knowledge graph construction. Drive initiatives to measure, monitor, and improve data quality, model quality, and end-to-end system performance. Collaborate with cross-functional teams to deeply understand customer pain points and deliver high-quality, production-ready ML solutions. Mentor junior engineers or learn from experienced ones in a tight-knit, high-velocity environment. About you: 3+ years of industry experience in AI or Machine Learning Engineering. BA/BS in computer science, math, sciences, or a related field. Experience with search, recommendation, natural language processing, or other large-scale ML systems. Proven ability to design, build, and ship production-ready models and systems. Demonstrated expertise in ML evaluation, benchmarking, and data quality—ideally with experience in building or maintaining evaluation frameworks for complex enterprise tasks. Proficiency in your ML framework of choice (e.g., TensorFlow, PyTorch). Strong coding skills (Python, Go, Java, C++, etc.). Thrive in a customer-focused, cross-functional environment; a proactive and positive attitude is a must. Location: This role is hybrid (4 days a week in our Mountain View, CA office) Compensation & Benefits: The standard base salary range for this position is $200,000 - $300,000 annually. Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. We offer a comprehensive benefits package including competitive compensation, Medical, Vision, and Dental coverage, generous time-off policy, and the opportunity to contribute to your 401k plan to support your long-term goals. When you join, you'll receive a home office improvement stipend, as well as an annual education and wellness stipends to support your growth and wellbeing. We foster a vibrant company culture through regular events, and provide healthy lunches daily to keep you fueled and focused. We are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organization. We're committed to an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race. LI-HYBRID AI-First Mindset at Glean: At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required. Global Data Privacy Notice for Job Candidates and Applicants: Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy . By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement . By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement , and I agree to the terms.

We exist to wow our customers. We know we’re doing the right thing when we hear our customers say, “How did we ever live without Coupang?” Born out of an obsession to make shopping, eating, and living easier than ever, we’re collectively disrupting the multi-billion dollar e-commerce industry from the ground up. We are one of the fastest-growing e-commerce companies that established an unparalleled reputation for being a dominant and reliable force in South Korean commerce. We are proud to have the best of both worlds — a startup culture with the resources of a large global public company. This fuels us to continue our growth and launch new services at the speed we have been since our inception. We are all entrepreneurs surrounded by opportunities to drive new initiatives and innovations. At our core, we are bold and ambitious people that like to get our hands dirty and make a hands-on impact. At Coupang, you will see yourself, your colleagues, your team, and the company grow every day. Our mission to build the future of commerce is real. We push the boundaries of what’s possible to solve problems and break traditional tradeoffs. Join Coupang now to create an epic experience in this always-on, high-tech, and hyper-connected world. Role Overview As our Staff Machine Learning Engineer for Coupang Media Group, Ads Quality, you will be responsible for developing, designing machine learning models, optimization algorithms and new product features for our advertising platform. The Coupang Media Group (CMG) is responsible for providing vendors that sell their products on Coupang a host of marketing products and services.  We launched Product Ads performance advertising marketplace late in 2018.  We have grown rapidly to over 10,000 active advertisers and 150M daily ad impressions in only 5 months’ time. Our group is responsible for advertising brands and products inside our e-commerce website. To get an idea of how valuable that is, both Alibaba and Amazon make more money on advertising than on selling products. This is an opportunity to be part of building up a new development organization with strong revenue potential inside a successful company. We’re building full-stack engineering teams to handle large scale advertising problems, including real-time behavioral targeting, auctioning and bidding systems and search based advertising. We are in the growth phase of the organization, so you’ll be a part of managing the transition from growth phase to enterprise processes. In this role you will be responsible for innovating and building our new ad quality stack for our ads platform  and eventually extend it into a full ad exchange, DSP, and SSP. We currently handle about 1bn impressions per day from internal traffic alone. What You Will do Design features and build large-scale machine learning models and systems to improve ad targeting, relevance, ranking, and engagement Design and implement large-scale ML systems for search ranking, semantic retrieval, query understanding, and personalized product discovery using state-of-the-art techniques such as transformer-based models, contrastive learning, and vector search Drive innovation in search relevance and user intent modeling using large language models (LLMs), embedding-based retrieval, and multi-modal learning Build and optimize ML pipelines using tools such as Apache Spark, Airflow, Kubeflow, and MLflow, ensuring reproducibility, scalability, and operational excellence Define and track key performance metrics to evaluate model impact and identify high[1]leverage opportunities for improvement Collaborate cross-functionally with product, engineering, and data science teams to align technical solutions with business goals and customer experience Mentor and grow engineering talent, fostering a culture of technical excellence, experimentation and continuous learning Basic Qualifications Bachelor's degree in computer science, electrical engineering, mathematics, statistics or closely related fields 4 years of professional experience in applied machine learning Experience in machine learning, deep learning, and statistical modeling Proficiency in Python and/or Java, with experience in building production grade ML systems Preferred Qualifications Master’s or PhD in relevant technical fields Experience with search systems, information retrieval or recommendation engines Experience with LLMs, embeddings, and vector search technologies Experience with cloud platforms such as AWS, Google Cloud Platform including services like Vertex AI, BigQuery or SageMaker Experience working in startup or high-growth environments Proven ability to lead-cross-functional teams and deliver results in a multicultural, global organization Hands-on experience with modern ML frameworks such as TensorFlow, PyTorch, Scikit[1]learn, Keras, XGBoost, LightGMB, and H2o.ai Experience with ML lifecycle tools such as MLflow, Kubeflow, Weights & Biases, or Amazon SageMaker Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders Demonstrated ability to work independently and manage ambiguity in fast-paced environments Pay & Benefits Our compensation reflects the cost of living across several US geographic markets. At Coupang, your base pay is one part of your total compensation. The base pay for Staff position ranges from $152K/year to $261K/year. Pay is based on several factors including market location and may vary depending on job-related knowledge, skills, and experience. General Description of All Benefits Annual bonus is 0-20% of the base salary Medical/Dental/Vision/Life, AD&D insurance Flexible Spending Accounts (FSA) & Health Savings Account (HSA) Long-term/Short-term Disability Employee Assistance Program (EAP) program 401K Plan with Company Match 18-21 days of the Paid Time Off (PTO) a year based on the tenure 12 Public Holidays 6 weeks Paid Parental leave Pre-tax commuter benefits MTV - [Free] Electric Car Charging Station General Description of Other Compensation “Other Compensation” includes, but is not limited to, bonuses, equity, or other forms of compensation that would be offered to the hired applicant in addition to their established salary range or wage scale. Coupang is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to actual or perceived race (including traits historically associated with race, including but not limited to hair texture and protective hair styles), color, religion, religious creed (including religious dress and grooming practices), sex or gender (including pregnancy, childbirth, breastfeeding, and medical conditions related to pregnancy, childbirth or breastfeeding), gender identity, gender expression, sexual orientation, ,ancestry, national origin (including language use restrictions), age (40 and over), physical or mental disability, medical condition, genetic information, HIV/AIDS or Hepatitis C status, family status (including but not limited to marital or domestic partnership status), military or veteran status, use of a trained dog guide or service animal, political activities or affiliations, ancestry, citizenship, family and medical leave status, status as a victim of any violent crime, or any other characteristic or class protected by the laws or regulations in the locations where we operate. Recruitment Process and Others Recruitment Process Application Review - Phone Interview - Onsite (or Virtual Onsite) Interview – Offer The exact nature of the recruitment process may vary according to the specific job and may be changed due to scheduling or other circumstances. Interview schedules and the results will be informed to the applicant via the e-mail address submitted at the application stage. Details to Consider This job posting may be closed prior to the stated end date for application if all openings are filled. Coupang has the right to rescind an offer of employment if a candidate is found to have submitted false information as part of the application process. Those eligible for employment protection (recipients of veteran’s benefits, the disabled, etc.) may receive preferential treatment for employment in accordance with applicable laws. Privacy Notice Your personal information will be collected and managed by Coupang as stated in the Application Privacy Notice located below: https://www.coupang.jobs/privacy-policy/ If you need assistance and/or a reasonable accommodation in the application of recruiting process due to a disability, please contact us at usrecruiting@coupang.com . Requisition ID: R0072711

About Hightouch Hightouch is an Agentic Marketing Platform powered by the industry-leading Composable CDP. With complete brand context, customer data, and performance history in one place, every marketer finally has the power to build and ship end-to-end campaigns themselves. Teams move faster, stay on brand, and get AI marketing that actually works. Founded in 2019 and headquartered in San Francisco, Hightouch enables marketing teams to analyze performance, brainstorm ideas, and generate creative at a speed and quality that wasn't previously possible. Named a Leader in the 2026 Gartner® Magic Quadrant™ for Customer Data Platforms, Hightouch is trusted by leading enterprises like Domino's, Spotify, Aritzia, Cars.com, Ramp, and PetSmart. At Hightouch, our mission is to help our customers leverage data and AI to grow their businesses. The team is ambitious, impact-driven, efficient — and we believe humility, kindness, and compassion are essential to our success. If you're energized by velocity, obsessed with raising the bar, and want to build alongside people who care deeply about each other and our customers, we'd love to meet you. About the Role We are looking for an engineering leader to lead machine learning efforts across Hightouch. While hundreds of companies use Hightouch today to sync data into their SaaS systems to automate and improve operations, there’s a lot of surface area we haven’t touched in helping companies figuring out which customers to message, what content to put in messages, and when to send messages. A lot of this work today is done manually through intuition and guesswork, and we believe that adding machine learning could have a step function impact for our customers. And given our access to data warehouses and databases, Hightouch is perfectly placed to make use of a company’s customer data in building a powerful intelligence layer. At Hightouch, our engineering leaders serve as both active technical voices and strong people managers. The key outcomes we are looking for from this role include: Product Development: Lead the team in roadmapping and executing successful development of significant features and products as required to drive the business forward. Uphold a high technical bar, while making pragmatic tradeoffs Pace: Through a combination of leadership and technical expertise, improve the pace of execution of the team Recruiting: Be responsible for growing the team, including identifying needs, deciding how to evaluate candidates, and ensuring we hire high impact team members Reliability: Against the uphill battle of high growth/scale, up-level the team on reliability, resulting in fewer incidents, catching our own issues before our customers do, and a strong incident management hygiene People: Foster growth of team members and ensure strong engagement and motivation Some of the problems we’ll be working on include: Personalization and Product Recommendation : There are often many options for what content a company could message a user with, including which products to show from catalogues. Given this large state space, how can Hightouch help personalize messages with the most relevant content for each user? Probabilistic Identity Resolution: Many of our customers have multiple datasets and sources of information on their users. How do we stitch these datasets together and decide which profiles are from the same user or household? Content Generation : Particularly with recent advances in LLMs, how can we help marketers generate text, images, and creatives that are compelling to their customers? ML Infrastructure: Building the infrastructure to support model training and inference at scale for some of the largest companies in the world. What We Offer We are looking for talented, intellectually curious, and motivated individuals who are interested in tackling the problems above. This is a senior role, but we focus on impact and potential for growth more than years of experience. The salary range for this position is $230,000 - $400,000 USD per year, which is location independent in accordance with our remote-first policy. We also offer meaningful equity compensation in the form of ISO options, and offer early exercise and a 10 year post-termination exercise window. We have limited inbound applications to one application per candidate. You will be auto-rejected if you apply to multiple roles. Please only apply to the position you are most qualified for. E-Verify Statement Hightouch participates in E-Verify. After you join the team, we'll verify your eligibility to work in the U.S. by submitting information from your Form I-9 to the Social Security Administration and, if needed, the Department of Homeland Security. This process happens post-hire only — we never use E-Verify to pre-screen applicants. E-Verify Notice E-Verify Notice (Spanish) Right to Work Notice Right to Work Notice (Spanish)

About Lightfield Lightfield is an AI-native CRM that assembles itself from your email, calendar, and meetings. It captures every interaction and turns it into organized context: accounts, tasks, follow-ups, and insights, so nothing slips through the cracks. We’re rethinking CRM from first principles. Instead of forcing teams to maintain rigid systems, Lightfield learns from how companies actually work, adapting, automating, and surfacing the insight that drives growth. We’re building the CRM platform we always wished existed: fast, intelligent, and genuinely helpful. We are backed by Greylock, Lightspeed, and Coatue, and our founders previously built Tome, a generative AI presentation product used by over 25 million people. Before Lightfield, our team worked on Llama, Instagram, Facebook Messenger, Pinterest, Google, and Salesforce. About the role Lightfield's AI/ML team builds the experiences at the core of our product, developing new applications to wow our customers. Today, the team is focused on building a powerful, domain-specific AI that outperforms generic LLMs We’re inspired by the challenge of creating innovative new AI products for people doing serious work, and we’re looking to grow our AI/ML team to meet that challenge. What you'll do Lead the development of ML product development infrastructure, focusing on scaling and innovating in areas of collaboration and versioning, particularly in the context of LLM model training and prompting Create and maintain a platform that will be used by multiple teams working on ML products, ensuring its scalability, efficiency, and user-friendliness. Collaborate closely with internal teams to integrate ML solutions and define best practices for software engineering in an AI-driven development landscape. Help build a world-class AI/ML engineering team by recruiting and mentoring teammates Address and solve open-ended technological challenges in software engineering at scale, especially in the context of AI-driven systems. Who You Are You have a BS or MS degree in CS, Engineering, AI or a related field. 6+ years experience in software engineering with a focus on ML infrastructure. You have a strong understanding of deep learning AI/ML frameworks or cloud services Experience with the integration of software engineering with large language models. Ability to navigate and solve open-ended technological challenges in a fast-evolving AI landscape. Excellent collaboration skills, with the ability to work effectively with both internal teams and external partners. Strong problem-solving skills and the ability to handle complex, cross-functional projects. Bonus Points Publications in applied AI/ML scientific journals Experience navigating open source/vendor solutions in LLM ops space (LangChain, Llama, Pinecone, etc) Benefits & Perks Competitive salary Meaningful early equity Health insurance (medical, dental, vision) 3 weeks of PTO 11 paid company holidays + we enjoy a winter holiday break 3 months of paid family leave Wednesdays work from home Regular team dinners, events, offsites, and retreats 401k plan Other perks include: commuter and lunch stipend

About Bree Bree is a consumer finance platform building faster, simpler, and more affordable financial services for Canadians who often live paycheck to paycheck. We operate in a massive market that’s historically been underserved by traditional financial institutions, and we’re building products that help customers access short-term credit with a transparent, user-first experience. To date, 800,000+ Canadians have signed up for Bree—and we believe we’re still early. We’re at an exciting intersection of product-market fit, rapid growth, and a clear path to becoming one of the most important fintech companies in Canada. We’re at 8-figures of annualized revenue, growing quickly, and profitable. We were part of Y Combinator (Summer 2021) and raised a $2M seed round shortly after. About the Role We’re looking for a Machine Learning Engineer to build and scale high-impact, world-class ML systems. You’re passionate about deploying AI solutions, optimizing performance, and driving measurable results. Your work will power critical decisions and shape the future of our technology. What You'll Do Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference. Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies. Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques. Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation. Apply machine learning design patterns to build modular, reusable, and production-ready models. Collaborate with data engineers to develop high-performance data pipelines for training and inference. Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes. Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques. What You'll Need Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch. Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques. Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows. Hands-on experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL). Knowledge of cloud-based ML deployment and infrastructure management. Ability to implement real-time and batch inference pipelines efficiently. Strong analytical and problem-solving skills to translate business needs into scalable ML solutions. Eagerness to work in a fast-paced environment and continuously refine ML processes for efficiency and accuracy. Benefits: 💰Top of the market compensation for top performers ⚕️Comprehensive health, dental, and vision benefits plan 🖥 $1,500 annual learning & home-office stipend 🧘🏼 $1,000 annual wellness stipend 🍔 Monthly Lunch Stipend 🚗 Commuter Benefits 🚼Paid Parental leave 🏝20 annual PTO days + unlimited sick days 🚀 Quarterly Team Gatherings ☕ In Office Amenities

About Us Relace is building the models and infrastructure that code agents reach for. We power the fastest model on OpenRouter (10,000 tok/s) and deliver optimized small language models designed for retrieval, application, and core code generation functions. Our technology supports some of the world’s fastest-moving companies — including Lovable, Figma, and Vercel — as they deploy and scale code generation to hundreds of millions of users. We recently raised our Series A from a16z, and we’re growing quickly. Our team is made up of mathematicians, physicists, and computer scientists who are deeply passionate about their craft. If you thrive on ambitious technical problems, care about elegant systems design, and want to build the foundation of how code gets written at scale, this is the place for you. The Role We’re looking for a Machine Learning Engineer who loves getting close to the metal. This is a hands-on engineering role focused on making models faster, more efficient, and more reliable through low-level optimizations and smart systems design. The ideal candidate is excited by CUDA kernels, memory layouts, GPU scheduling, and squeezing performance out of complex training and inference workloads. They should be just as comfortable optimizing compute and networking paths as they are working alongside research teams to productionize new architectures. This is a role for someone who enjoys deep performance tuning, understands the realities of running large-scale ML systems, and thrives in fast-moving, high-leverage environments. Requirements Strong background in systems-level ML engineering. Experience with CUDA, GPU kernel optimization, and performance tuning. Fluency in Python and at least one systems language (C++ or Rust preferred). Familiarity with distributed training frameworks (e.g., PyTorch, JAX, DeepSpeed, or similar). Experience working with large-scale training or inference infrastructure. Understanding of memory management, parallelization, and hardware-aware model optimization. 2+ years of experience working in ML infrastructure or performance-critical environments. Willingness to work in-person from our SF office in FiDi.

About Sandbar Sandbar is an interface company in New York City. We aim to augment individuals so we can each think, act, and move more freely. Our team has built SW, ML, and HW products across Meta, CTRL-labs, Google, Apple, Fitbit, Peloton, and Equinox. Our first product, Stream, is a self extension—a private voice ring and conversational interface. Stream has been featured in WSJ, Bloomberg, & Wired, and begins shipping in Summer '26. Join us in creating technology that extends human thinking. About We’re looking for a machine learning engineer to help build Stream, a new conversational computer. As a machine learning engineer, you will develop a system which spans voice, memory, and agentic control. This role is perfect for someone cares deeply about real-world ML deployment and human-in-the-loop agentic interactions. Responsibilities Develop, evaluate, and deploy audio models spanning cloud models to resource-constrained on-device settings Optimize inference pipelines for latency, reliability, and concurrency Work closely with other ML/AI engineers, infrastructure engineers, designers, and cofounders on existing and future products Qualifications 4+ years in machine learning experience Experience shipping ML-based products is required Experience developing audio / voice models is required Passion for human-computer interaction FTE Benefits Health, vision, and dental benefits Company-sponsored 401(k) Unlimited PTO and sick time Early stage equity

Who we are: Sardine is the leading agentic risk platform for fighting financial crime. Our integrated solution unifies data across risk teams to help organizations stop fraud in real time, prevent AI-driven attacks, and automate fraud and AML operations. Sardine’s platform is strengthened by one of the fastest-growing fraud consortiums in the market, spanning more than 6 billion profiled devices, 800 million consumers, and 3 million businesses worldwide. Leading companies including FIS, GoDaddy, Intuit, Edward Jones, ZoomInfo, and Checkout.com rely on Sardine to secure and grow trust in their products. Our culture: We have hubs in the Bay Area, NYC, Austin, Toronto, and São Paulo. However, we maintain a remote-first work culture. #WorkFromAnywhere We hire talented, self-motivated individuals with extreme ownership and high growth orientation. We value performance and not hours worked. We believe you shouldn't have to miss your family dinner, your kid's school play, friends get-together, or doctor's appointments for the sake of adhering to an arbitrary work schedule. Location: Remote - United States or Canada From Home / Beach / Mountain / Cafe / Anywhere! We are a remote-first company with a globally distributed team. You can find your productive zone and work from there. About The Role As a Machine Learning Engineer, you’ll do more than build models - you’ll design the systems that make fraud detection possible. You’ll work across modeling, data pipelines, and backend systems (Go) to ensure ML models run reliably, efficiently, and at scale. This is a chance to combine applied ML with large-scale systems engineering, owning end-to-end solutions that tackle high-stakes, ever-evolving challenges. What you’ll be doing: Build and optimize data pipelines and backend services to process device and behavioral data in real time. Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production. Turn raw data into production-ready features that feed our fraud detection systems. Collaborate with platform and backend engineers to integrate models seamlessly. Maintain high standards of security, privacy, and compliance. Champion best practices in testing, documentation, and observability. What you’ll need: 5+ years in software engineering, with strong backend experience (Go or Python). Hands-on experience with applied ML using large datasets (PyTorch, Scikit-learn, etc.). Strong SQL skills and familiarity with relational and non-relational databases. Experience with end-to-end ML systems: feature pipelines, model deployment, monitoring, and iteration. Excellent communication skills in English, both written and verbal. Bachelor's or Master's in Computer Science, Engineering, or a related discipline. Bonus Points Domain knowledge in fraud, risk, or cybersecurity. Familiarity with CI/CD, Docker, Kubernetes and the modern devops framework. Understanding of modern browser APIs and high-entropy data collection techniques. Familiarity with leveraging frontier LLMs for automation. Benefits we offer: Generous compensation in cash and equity Early exercise for all options, including pre-vested Work from anywhere: Remote-first Culture Flexible paid time off and Year-end break Health insurance, dental, and vision coverage for employees and dependents - US and Canada specific 4% matching in 401k / RRSP - US and Canada specific MacBook Pro delivered to your door One-time stipend to set up a home office — desk, chair, screen, etc. Monthly meal stipend Monthly social meet-up stipend Annual health and wellness stipend Annual Learning stipend Join a fast-growing company with world-class professionals from around the world. If you are seeking a meaningful career, you found the right place, and we would love to hear from you. To learn more about how we process your personal information and your rights in regards to your personal information as an applicant and Sardine employee, please visit our Applicant and Worker Privacy Notice .

Company Mission / Why This Matters Two Dots builds verification and risk infrastructure for housing to help solve the housing crisis. Housing is too expensive because America created a single family mortgage machine to cut average people into home price inflation fueled by soft bans on new development. That worked for many decades, but when a small single family home costs several million dollars, it stops being an engine of opportunity and becomes a source of the very resentment modern mortgages were originally created to solve. Housing supply has been restricted so much that people have started fabricating documentation or relying on bypasses and overrides to sign up for a payment they can’t really afford. That conceals the problem instead of solving it. We believe that public and private policy has to change, and that involves breaking the system that conceals our affordability crisis and leaves people without the disposable income required to live satisfying lives, fueling resentment and political instability that turns problems at home into problems for the world. The Role Two Dots is hiring a Machine Learning Engineer for a low-headcount, high-impact role focused on technically difficult applied ML problems in housing verification, underwriting, fraud detection, and document understanding. This is not a research role, although the right person has the depth to develop models from scratch end-to-end. Some of the problems we are facing are genuinely hard: detecting whether a PDF was forged or edited, inferring latent financial profiles from messy payment data, extracting information from noisy documents with very high reliability, and solving chatbot or agent quality problems that big foundation models do not solve out of the box. They should be math literate, comfortable with PyTorch, evaluation, model deployment, quality management, metrics-driven evaluation, and data warehouse-oriented SQL such as BigQuery. What You'll Work On Document forensics and detecting fraudulent or edited PDFs Cash flow underwriting: inferring a latent financial profile from paystubs, bank statements, business data, or other payment data Extracting information from unstructured or noisy sources with very high reliability Solving chatbot and agent quality problems that are too hard for others to solve Developing models, evaluation systems, and quality management processes from scratch Creating broad-based, systemic improvements in ML, LLM, and agent performance Educating the team on how to evaluate ML pipelines and workflows, including workflows that involve prompting foundation models The Team Henson (CEO) started his career selling FX derivatives to hedge funds at Goldman, then worked at a real estate tech startup for several years leading sales. This enables him to engage with the largest institutional property managers and real estate investors in the country and create value through those relationships. Max (CTO) started out as a software engineer at Blend, a mortgage application company that went public, and went on to work on the search team at Google. That combination of specific consumer fintech experience and knowledge of how sophisticated ML products succeed in production made big enterprise deals work from day 1. We met in middle school and created a media website together where people could watch and post their flash games and animations. We learned to code, source talent, and forge partnerships - and had 500 active users. Although a tragic addiction to World of Warcraft interrupted work on the website, we got back together to start Two Dots. Other team members include: Meta ML alumnus with decades of experience, a 21 year old UMich grad who was a top 2,000 LoL player (he is no longer playing the game, thank god), and a former agave farmer who started a shipping and logistics company while at Stanford. What We're Looking For You should be able to take an ambiguous problem, like PDF fraud detection, and turn it into a reasonable technical plan without needing a well-defined box. You should understand the company strategy well enough to know what is more and less likely to be valuable in ML without escalating every decision or planning process to the most senior levels of management. You should have a strong command of: Tensors, PyTorch, training loops, and model deployment Metrics-driven evaluation and rigorous quality management Statistics, regularization, overfitting, training schedules, and GPU memory management Computer vision, NLP, and multimodal understanding problems Data warehouse-oriented SQL, especially BigQuery Explore-vs-exploit tradeoffs in applied ML work You should be interested in the company mission through a technical lens: consumer underwriting, document understanding, fraud detection, multimodal understanding, and systems that reveal rather than conceal the real affordability crisis in housing. Despite the more cerebral nature of the role, this is an applied and impact-focused position. The work requires patience with exploration, but also the judgment to know when a good-enough solution under time pressure is better than searching for a global optimum. About the Interviews ML phone screen If you do not know how PyTorch, training, and evaluation work, and cannot talk about real modeling work you have done, we will filter you out at this stage. Behavioral interview We will assess whether you are actually interested in working at a startup, whether you can deal with ambiguity, and whether you are more of a pure researcher than an applied builder. ML foundations interview We will test rigorous knowledge of math, statistics, ML foundations, metrics and evaluation, tensors, regularization, overfitting, training schedules, and GPU memory management. Ambiguous problem design We will ask you to convert a hard, ambiguous problem into a reasonable plan. Explore-vs-exploit judgment We will construct a scenario where you need to choose a good-enough solution under time pressure instead of searching for a global optimum.

Company Overview: Root Access is an applied AI company building developer tools. We help mission-critical hardware teams leverage purpose-built AI to program and certify their systems faster. Role Description: The Machine Learning Engineer will be responsible for designing and developing machine learning systems, implementing appropriate ML algorithms, conducting experiments, and improving the product. They work with data to create models, perform statistical analysis, and train and retrain systems to optimize performance. Their goal is to build efficient self-learning applications that will delight customers. This is an early-stage company with ambitious goals. You might be a good fit if you have: Proficiency in PyTorch and modern-transformer based systems Experience with AWS for scalable ML service deployment Experience building with Agentic AI frameworks (e.g., RAG, Langchain, MCP, etc) Have 1-3+ years of full-time experience in an MLE role What We’re Looking For: Strong ML Foundations - Experience with recommender systems, embeddings, foundation models. You understand when to use the fancy stuff—and when to keep it simple. Production Mindset - You’ve shipped ML systems that run in the real world. You write reliable Python, know your way around infra basics, and care about performance. Data Agility - You’ve worked with messy data—scraping, parsing, cleaning, and transforming it into something your models can learn from. Frontend Awareness - You’re not expected to be a frontend engineer, but you know how to make ML feel native in a modern React-based product. High Ownership DNA - You see the problem, spec the solution, and ship. You don’t need permission—you need a challenge. 1-of-1 Energy - You’ve been underestimated, or boxed in. You're ready to work somewhere that lets you fully show what you're capable of.

About Us: webAI is the first end-to-end private AI platform. Enterprises and Governments use webAI to bring AI to their data, powering specialized intelligence trained on their own knowledge and compounding in value with every use. Our approach is guided by a simple philosophy: AI should be specialized, sovereign, efficient, and sustainable. It should solve real problems, not just burn more compute. Our technology enables the development of powerful AI using limited amounts of data, challenging the underlying assumption that big data is the key to unlocking the full power of AI. We are offering a superior career opportunity to join a dynamic and fast-growing team that fosters an exciting and growth-oriented work culture; an opportunity where you can truly put your name on meaningful change for an entire civilization. About the Role: We are seeking an experienced Staff Machine Learning Engineer with a strong background in Large Language Models (LLMs) and/or Mixture of Experts (MoEs). The ideal candidate will have a proven track record of developing and deploying advanced AI models. Responsibilities: Lead the development and optimization of Large Language Models and Mixture of Experts models. Collaborate with cross-functional teams to integrate ML models into our platform. Conduct cutting-edge research in machine learning, with a focus on improving the performance and efficiency of LLMs. Stay abreast of the latest advancements in AI and ML, and apply this knowledge to improve our models and methodologies. Mentor junior engineers and contribute to the team’s knowledge sharing and best practices. Qualifications: Advanced degree (Ph.D. preferred) in Computer Science, or a related field. Proven track record of building and innovations through publications or industry experience. Minimum of 6 years of experience in machine learning, with specific expertise in Large Language Models and Mixture of Experts. Strong programming skills in Python and machine learning frameworks like TensorFlow and/or PyTorch. Demonstrated ability to lead complex projects and work collaboratively in a team environment. Excellent problem-solving skills and a passion for innovation. Preferred Skills: Experience with cloud computing services (AWS, Azure, GCP). Knowledge of Big Data technologies (Hadoop, Spark). Familiarity with containerization and orchestration technologies (Docker, Kubernetes). Publications or presentations in recognized Machine Learning journals or conferences. We at webAI are committed to living out the core values we have put in place as the foundation on which we operate as a team. We seek individuals who exemplify the following: Truth - Emphasizing transparency and honesty in every interaction and decision. Ownership - Taking full responsibility for one’s actions and decisions, demonstrating commitment to the success of our clients. Tenacity - Persisting in the face of challenges and setbacks, continually striving for excellence and improvement. Humility - Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others. Benefits: We strive to provide competitive benefits to all employees. The benefits listed in this posting generally apply to U.S.-based employees. For employees hired outside the United States, benefits may vary based on local law, country-specific requirements, and the employment platform or entity through which the employee is hired. Competitive salary Comprehensive health, dental, and vision benefits package 401(k) match Equity options $200/month Health & Wellness stipend Continuing Education support $500/year Function Health subscription Free parking for in-office employees Flexible Time Off (FTO) Parental leave for eligible employees Supplemental life insurance webAI is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAI to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.

Hadrian - Manufacturing the Future Hadrian is building autonomous factories that help aerospace and defense companies manufacture rockets, satellites, jets, and ships up to 10x faster and up to 2x cheaper. By combining advanced software, robotics, and full-stack manufacturing, we are reinventing how America produces its most critical parts. We’re accelerating our mission with the launch of Factory 3 in Mesa, Arizona, a 290,000-square-foot facility creating 350 new jobs. We are expanding rapidly to support thousands of future hires, launching Hadrian Maritime to expand into naval production, and introducing a Factory-as-a-Service model that delivers complete systems instead of individual parts. Hadrian is backed by leading investors including T. Rowe Price, Lux Capital, Founders Fund, and Andreessen Horowitz, our fast-growing team is united around reindustrializing American manufacturing for the 21st century and beyond. The Role: Hadrian is building advanced software systems to automate Design for Manufacturing (DFM) analysis and accelerate precision manufacturing. Our homebuilt DFM platform analyzes design prints and CAD models to make logical, data-driven determinations about whether and how Hadrian can manufacture a part. We work closely with some of the best operators in the world to identify high-impact opportunities where software, computer vision, and machine learning can meaningfully augment or automate complex engineering judgment. As a Senior Machine Learning Engineer, you will play a key role in designing, building, and scaling these systems end to end. What You’ll Do: Research, develop and deploy cutting-edge deep learning models, including OCR, vision-language, and detection models for document understanding and layout analysis Contribute to the ongoing development and refinement of current models in close collaboration within the engineering team and end users Work in every facet of the Machine Learning lifecycle - including the creation and optimization of production data pipelines, and software systems for training, inference, labeling, and evaluation Judiciously combine open-source solutions and novel research to create world-class vision systems for manufacturing applications What We’re Looking For: Bachelors in Computer Science, Electrical Engineering, Mechanical Engineering (or similar discipline), or an equivalent amount of deep learning experience 5+ years of experience in Computer Vision and/or Machine Learning, including ownership of projects throughout the entire ML Lifecycle Proficiency in Python, OpenCV, SQL, and one or more deep learning frameworks (PyTorch, Tensorflow, etc.) What sets you apart: A Master’s Degree in Computer Science with focus in Artificial Intelligence or Machine Learning Demonstrated experience with detection/segmentation models and achieving high performance results - including exploratory analysis, model selection, and hyperparameter tuning Previously worked in aerospace, defense, or manufacturing, and have experience working 3D/CAD/CAM data for manufacturing applications Strong software engineering skills and prior web development experience using Javascript/TypeScript or Python, and familiarity with FastAPI or Express frameworks Prior experience with distributed training using cloud infrastructure Prior experience with visual document understanding and layout analysis Published research or achieved SOA results on an ML application Prior experience with multi-modal deep learning models You have a passion for manufacturing and believe that the industry needs better software Prior experience working in a startup environment Compensation For this role, the target salary range is $160,000 - $250,000 (actual range may vary based on experience). This is the lowest to highest salary we reasonably and in good faith believe we would pay for this role at the time of this posting. We may ultimately pay more or less than the posted range, and the range may be modified in the future. An employee's pay position within the salary range will be based on several factors, including, but not limited to, relevant education, qualifications, certifications, experience, skills, geographic location, performance, and business or organizational needs. Benefits for Full-time Employees Medical, dental, vision, and life insurance plans for employees 401k Relocation support may be provided for certain situations, based on business need. Flexible vacation policy Equity ITAR Requirements To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State. Learn more about the ITAR here . Hadrian Is An Equal Opportunity Employer It is the Company’s policy to provide equal employment opportunity for all applicants and employees. The Company does not unlawfully discriminate on the basis of race inclusive of traits historically associated with race (including, but not limited to, hair texture and protective hairstyles, such as braids, locks and twists), color, religion, sex (including pregnancy, childbirth, or related medical conditions), gender identity, gender expression, transgender status, national origin (including, in California, possession of a drivers license), ancestry, citizenship, age, physical or mental disability, height or weight, medical condition, family care status, military or veteran status, marital status, domestic partner status, sexual orientation, genetic information, exercise of reproductive rights, any other basis protected by local, state, or federal laws, or any combination of the above characteristics. When necessary, the Company also makes reasonable accommodations for disabled candidates and employees, including for candidates or employees who are disabled by pregnancy, childbirth, or related medical conditions.

Patreon is a media and community platform where over 300,000 creators give their biggest fans access to exclusive work and experiences. We offer creators a variety of ways to engage with their fans and build a lasting business including: paid memberships, free memberships, community chats, live video, and selling to fans directly with one-time purchases. Ultimately our goal is simple: fund the creative class. And we're leaders in that space, with: $10 billion+ generated by creators since Patreon's inception 100 million+ free memberships for fans who may not be ready to pay just yet, and 25 million+ paid memberships on Patreon today. We're continuing to invest heavily in building the best creator platform with the best team in the creator economy and are looking for a Senior Machine Learning Engineer to support our mission. This role is based in San Francisco or New York as an in-office 2 days per week on a hybrid work model . About the Team You'll join the Relevance team, whose mission is to build the ML systems that power how fans discover creators and how content surfaces across Patreon. The team is responsible for search, ranking, feed relevance, and creator-fan matching. You'll work closely with a small, collaborative group of MLEs on shared infrastructure, code reviews, and roadmap alignment, while partnering cross-functionally with Product, Data Engineering, and Trust & Safety to deliver measurable impact across the platform . About the Role Conduct exploratory data analyses and proof-of-concept machine learning models to understand opportunities and potential project impact. Collaborate with cross-functional partners, such as product, engineering, design, legal, and trust and safety to design effective machine learning solutions. Analyze and prepare training data, including using crowdsourcing data labeling techniques. Train and iterate on machine learning models using novel techniques. Deploy machine learning models to production and write backend code when necessary to properly deploy the model. Debug models when observability shows performance gaps, and iterate on models. What you'll need Experience working in an end-to-end machine learning team environment (typically 5+ years): analyzing data, building and iterating on machine learning models, writing production-level code and shipping to production, monitoring performance, and A/B testing. Writes clean and robust code in Python or other programming languages, and provides substantive, constructive feedback in code reviews. Experience debugging complex systems with a systematic approach. Analyzes datasets thoroughly to develop product and customer insights. Writes clear technical documentation for both technical and non-technical audiences. Seeks and incorporates feedback on code, models, and technical approaches. Bachelor's degree in Computer Science, Computer Engineering, or a related field, or the equivalent We hire talented and passionate people from different backgrounds because workplace diversity and inclusion is critical to our ability to serve creators worldwide. If you’re excited about a role but your past experience doesn’t match with every bullet point outlined above, we strongly encourage you to apply anyway. If you’re a creator at heart, are energized by our mission, and share our company values, we’d love to hear from you. About Patreon Patreon powers creators to do what they love and get paid by the people who love what they do. Our team is passionate about making this mission and our core values come to life every day in our work. Through this work, our Patronauts: Put Creators First | They’re the reason we’re here. When creators win, we win. Build with Craft | We sign our name to every deliverable, just like the creators we serve. Make it Happen | We don’t quit. We learn and deliver. Win Together | We grow as individuals. We win as a team. Patreon is proud to be an equal opportunity employer. We provide employment opportunities without regard to age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, veteran status, or any other protected class. If you need a reasonable accommodation during the interview process, please let us know via email at accommodations@patreon. Patreon offers a competitive benefits package including and not limited to salary, equity plans, healthcare, flexible time off, company holidays and recharge days, commuter benefits, lifestyle stipends, learning and development stipends, patronage, parental leave, and 401k plan with matching. Patreon operates under a hybrid work model, where employees based in office locations are expected to come into the office two days per week, excluding sick time and paid leave. The goal of this policy is to be intentional about the in-person time we spend together to strengthen the feeling of community at Patreon. Candidates hired into remote-eligible roles are not expected to meet the same requirements. At Patreon, we believe in fair and transparent pay. In compliance with New York and California pay transparency laws, we are sharing the expected salary range for this role. The posted salary range is dependent on the location and the level. This range may encompass multiple levels within the role’s job family. The final offer will be based on candidate’s experience, skills, competencies, and geographic location, aligning with the appropriate job level within Patreon’s leveling framework. For remote employees located outside CA and NY, salary may vary based on location and local market conditions. Patreon reserves the right to modify or update compensation and benefits at any time

Imagine yourself designing and implementing the next breakthrough AI/Machine-Learning services, enabling AI for everyone at the start of the company's explosive growth. At super.AI, we are building services to enable AI/Machine Learning technologies for the masses. We are at the exciting stage where the growth of our company forces us to do aggressive hirings to realize our visions quicker. We are a group of ex-Google, Microsoft, Amazon, and MIT engineers and data scientists who spent the last 2 years creating the new programming interface for AI. What you'll do: Design/implement a system orchestrating machine learning workflows Build SDKs to enable engineers using our platform easily Build tools to enable our AI engineers to be efficient in developing new AI solutions Explore ideas for practical and reliable models for data label quality and various label supervision sources. What we are looking for: Smart, humble, hardworking, and collaborative By  smart  we don't necessarily mean high IQ (although a huge plus), but an affinity toward learning. We want to automate as much as possible so we can focus on the few things that require human intelligence and creativity. By  humble  we don't mean submissive or unambitious, we mean it in the same way Roger Federer is humble. By  hardworking , we don't mean long hours (be with your family). What we mean is while you're here you're doing the best work of your life. It means being disciplined, professional, focused, gritty, resilient, and resourceful. By  collaborative , we don't mean submissive or deferential, it means taking leadership from everywhere. "I'm taking responsibility to fix this system", if there's a lack of trust "I'm going to address it" if goals are unclear "I'm going to deal with it", it's about being a meritocracy All four of the above are required . We all know people missing one of these and it's not nice to work with them (e.g. someone who is smart and hardworking but not humble and collaborative). Paradoxical The people we like the most are smart but humble Wants to win no matter what, but will not cross lines of integrity to get there Strong beliefs weakly held, etc. 3+ years of software engineering experience Solid foundational knowledge of Python Familiarity with distributed system and cloud services (AWS, Azure, GCP) is a plus Solid understanding of machine learning principles Familiarity with container frameworks such as Docker and Kubernetes Experience with the latest machine learning infrastructure developments is a plus If this resonates with you, it would be amazing to get to know each other.

JOB SUMMARY: The research team at ECRS is looking for a talented, experienced individual who is fearless in tackling real world problems with efficient, practical Machine Learning (ML) solutions and demonstrate excellence in computer vision . RESPONSIBILITIES: Research, design, develop and train new machine learning models or adapt and apply existing models and applications. Design or develop software systems using design and functional specifications, scientific analysis, good software development practices, and mathematical models. Write, update, and maintain computer applications and software packages to handle specific jobs based on machine learning. Develop and direct software system testing and validation procedures. Review and modify existing software to correct errors, adapt to new hardware, and upgrade interfaces and improve performance. Direct software programming and development of corresponding documentation. REQUIRED QUALIFICATIONS: Advanced degree in Computer Science, Statistics, Mathematics or similar, or a comparable career in the industry with an exceptionally good record of successful projects. Minimum 3-5 years of industry experience in computer vision delivering successful solutions. Good understanding of machine learning, statistics, and mathematics. Experience and exceptionally good proficiency in Python and Java, or C/C++. Proficient knowledge and experience with image classification, detection, tracking, etc. Proficiency in image/video processing and computer vision tools. Proficiency in machine learning tools and frameworks such as PyTorch , TensorFlow or similar. Experience working with edge devices. Experience with modern deep learning techniques in CV including convolutional networks, residual networks, attentional models, etc. Familiarity with machine learning workflow. Familiarity with cloud-based machine learning platforms. Experience in software testing and debugging including the use of automated testing processes. Familiarity with relational databases. Familiarity with SQL. Basic understanding of a retail environment & operations . Strong written and verbal communication skills . PREFERRED QUALIFICATIONS: Experience with deep learning infrastructure is a plus. Experience with NLP is a plus. Proficiency in statistical analysis is a plus. Experience with parallel computing technologies or distributed computing techniques is a plus. ALL APPLICANTS MUST BE AUTHORIZED TO WORK IN THE UNITED STATES. ABOUT ECRS: ECRS is a fast-paced, progressive technology company with a wide range of opportunities for quality-oriented, career-minded individuals. Geographically situated in the heart of the Blue Ridge Mountains, ECRS offers the unique opportunity of a high-tech career in a resort college town setting. The ECRS family is made up of energetic, outgoing professionals who love what they do for a living. They are courteous, knowledgeable people who strive for excellence in everything they do. ECRS employees work together in dynamic teams to create, sell, install, and support our best-in-class retail automation solutions. We believe that acceptance of diversity is a key reason as to why we're successful. All qualified applicants who can demonstrate integrity and competence will receive consideration for employment and advancement without regard to race, color, religion, gender, sexual orientation, disability, age, political affiliation, or national origin.

Blanc Labs is a premier partner for global enterprises, leading the way in digitization, automation, and the development of next-generation digital products and services. Our expertise in digital transformation powers businesses to accelerate service delivery, drive customer engagement, and foster growth. We are looking for a Senior Machine Learning Engineer to lead the end-to-end development and lifecycle management of machine learning models powered by proprietary data. This is a highly impactful role at the core of our AI ecosystem, responsible for building scalable, production-grade ML systems that directly power intelligent product features. You will collaborate closely with Data Scientists, AI Engineers, and Principal AI Engineers to identify opportunities, develop robust models, and ensure seamless integration into production systems. Key Responsibilities Design, build, and deploy end-to-end machine learning models using proprietary datasets Own the full ML lifecycle, including data preparation, feature engineering, model training, evaluation, and validation Collaborate with Data Scientists to identify and prioritize high-impact ML opportunities Work with Principal AI Engineers to ensure models align with system architecture and engineering best practices Package and expose trained models as scalable APIs for consumption by AI Engineers and downstream systems Build and maintain model pipelines, including versioning, retraining, and continuous improvement workflows Monitor model performance in production, ensuring accuracy, reliability, and relevance over time Troubleshoot model drift, data quality issues, and performance bottlenecks Qualifications Strong experience in machine learning engineering, with production deployment experience Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) Experience with data preprocessing, feature engineering, and model evaluation techniques Hands-on experience deploying ML models as APIs or microservices Familiarity with MLOps practices, including model versioning, monitoring, and retraining pipelines Experience working with large-scale or proprietary datasets Strong software engineering fundamentals (testing, scalability, performance optimization) Ability to collaborate effectively with cross-functional teams Nice-to-Have Experience with cloud platforms (AWS, GCP, or Azure) and ML services (e.g., SageMaker, Vertex AI) Familiarity with containerization and orchestration tools (Docker, Kubernetes) Experience with real-time or low-latency ML systems Exposure to LLMs or hybrid AI/ML systems Experience in building data pipelines (Airflow, Spark, etc.) Blanc Labs is an equal opportunity employer and is committed to employing in accordance with the Ontario Human Rights Code and the Accessibility for Ontarians with Disabilities Act. Accommodations within reason due to a disability or medical need are available on request for candidates taking part in the recruitment process. Blanc Labs is enabling a digital future. Headquartered in Toronto, we partner with clients in North & South America to digitize and automate their operations and build their next generation of digital products and services. We empower clients to enhance their digital offerings and bring creative solutions to the market faster. Learn more at  www.blanclabs.com .

The Company Toters is an on-demand e-commerce and delivery platform and operates a service that enables customers to get anything in their city at the highest level of convenience. At Toters, technology is at the heart of everything we do. We have product teams that are working hard every day to create products that make our customers' lives easier. Our engineers are also continuously creating solutions to make our processes more efficient, all in an effort to get to our customers fast and at the best cost. If you are interested in working in a high growth startup environment, and look to be part of a team that will potentially change the way customers shop in the Middle East, apply now. About the Role We are seeking hands-on experienced Senior ML/AI Engineer  to join our Product Platform Engineering team. You will design and lead production-grade machine learning systems supporting quick commerce and fintech platforms. The role involves owning the full lifecycle of ML models, working with large-scale datasets (10TB+), and driving strategic AI initiatives that create measurable business impact. Key Responsibilities Build and maintain end-to-end ML pipelines using XGBoost, LightGBM, PyTorch, and TensorFlow. Architect scalable training and inference systems for real-time predictions . Lead cross-functional ML initiatives and mentor engineering teams. Optimize models for demand forecasting, fraud detection, and personalization . Apply MLOps best practices and collaborate with DevOps on deployment. Process and analyze 10TB+ datasets using distributed computing frameworks. Required Qualifications 5+ years building production ML systems at scale (100K+ users). Expert in XGBoost, LightGBM, and gradient boosting algorithms . Strong Python and ML stack experience: PyTorch, TensorFlow, NumPy, Scikit-learn. Hands-on experience with agentic AI systems and MCP frameworks. Proven track record in ML deployment and MLOps . Technical leadership and mentorship experience. Preferred Qualifications Proficiency in Python . Experience in quick commerce or fintech ML applications . Familiarity with cloud platforms, microservices, or distributed systems . Research, publications, or open-source contributions in ML/AI. What We Offer Competitive IC5-level compensation and benefits . Office-first hybrid setup with remote options for exceptional candidates. Clear path to Principal Engineer or ML Architect . Opportunity to work on high-impact AI projects with executive visibility. Collaborative environment with mentorship and career growth opportunities .

About Niyam IT (Niyam) Niyam was founded in 2007 by a group of consultants who shared a unique vision: a technology company steeped in orderly process yet driven by passion and innovation. Over the following decade, we fine-tuned our craft and built an impressive track record of successful outcomes, securing our reputation as the go-to provider of smart, innovative solutions. Today, Niyam is at the forefront of the industry, leading the way in crafting mission-critical technologies for Emergency Preparedness & Response, Natural Resource Management, Law Enforcement & Justice, Health IT, and Global Citizen Services. What We Offer: Flexible Work Hours : Life doesn’t always fit into a 9-to-5 schedule. We offer flexibility to help you manage your work-life balance effectively. Remote Work : Niyam understands the value of flexibility. We offer remote work. Career Growth : Niyam is not just a job; it’s a career journey. We provide a supportive environment for your professional development and offer fully paid opportunities for training and advancement within the company. Great People : Our people are the blueprint of who Niyam is to the industry and community. Great Environment : Niyam fosters a great environment where innovation, collaboration, and personal growth thrive. Diversity & Inclusion : We believe in the strength of diverse perspectives. Your unique ideas are welcomed and celebrated every day at Niyam. Join us in creating a workplace where innovation, diversity, and well-being thrive. Your journey at Niyam awaits. Apply today! Niyam is seeking an AI/ML Engineer to join our team in support of our work with a federal client. This role will focus on designing, developing, and deploying advanced AI/ML solutions that enhance operational efficiency, enable data-driven decision-making, and support evolving mission needs. The ideal candidate brings a strong foundation in machine learning, data engineering, and MLOps, along with experience working in secure, regulated environments. This position requires collaboration across cross-functional teams and a commitment to delivering scalable, ethical, and compliant AI solutions. We offer competitive compensation and benefits. This full-time position will be hybrid to Ashburn, VA. This position is contingent upon award of contract. Roles and Responsibilities Design, develop, train, and validate advanced AI and machine learning models to support mission-critical use cases for a federal client, ensuring alignment with operational objectives and data governance standards. Evaluate and select appropriate machine learning techniques, algorithms, and neural network architectures (e.g., supervised, unsupervised, deep learning), leveraging frameworks such as TensorFlow and PyTorch to build scalable and efficient solutions. Perform end-to-end data lifecycle activities, including data collection, ingestion, cleansing, preprocessing, and feature engineering, ensuring data quality, integrity, and compliance with federal data management policies. Deploy AI/ML models into production environments, integrating with existing enterprise systems, cloud platforms, and APIs while ensuring high availability, scalability, and security. Establish and maintain MLOps pipelines to support continuous integration, continuous delivery (CI/CD), automated testing, model versioning, and performance monitoring across the model lifecycle. Monitor model performance over time, implement retraining strategies, and optimize models to ensure sustained accuracy, reliability, and operational effectiveness in dynamic environments. Identify, assess, and mitigate bias in datasets and model outputs, ensuring fairness, transparency, and adherence to ethical AI principles and applicable federal guidelines. Collaborate with cross-functional teams, including data engineers, software developers, cybersecurity personnel, and program stakeholders, to translate mission and business requirements into technical AI/ML solutions. Document model development processes, methodologies, and results to support auditability, reproducibility, and compliance with federal standards and accreditation requirements. Support security and compliance initiatives by aligning AI/ML solutions with frameworks such as NIST and FedRAMP, ensuring proper handling of sensitive data and adherence to system authorization processes (e.g., ATO). Qualifications and Education Requirements: US Citizenship with ability to obtain a Public Trust. Bachelor’s degree or higher in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical discipline from an accredited institution. 8+ years of progressive experience designing, developing, and deploying machine learning or artificial intelligence solutions within enterprise or mission-driven environments. Demonstrated experience with machine learning frameworks and tools such as TensorFlow, PyTorch, Scikit-learn, or similar technologies. Hands-on experience with data preprocessing, feature engineering, and working with large, complex datasets in both structured and unstructured formats. Proven experience deploying and operationalizing AI/ML models in production environments, including integration with cloud platforms (e.g., AWS, Azure, or GCP). Experience implementing MLOps practices, including CI/CD pipelines, model monitoring, versioning, and lifecycle management. Strong understanding of responsible AI practices, including bias detection, model explainability, and ethical AI considerations. Familiarity with federal security and compliance frameworks (e.g., NIST, FedRAMP) and experience working within regulated environments is preferred. Strong analytical, problem-solving, and communication skills, with the ability to effectively collaborate across technical and non-technical stakeholders. Local to Ashburn, VA and available to work onsite as needed. Preferred Skills: Experience supporting federal agencies or working within government contracting environments. Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes). Experience with big data technologies such as Hadoop, Spark, or distributed data processing frameworks. Familiarity with API development and microservices architecture. Experience implementing AI/ML solutions in cloud-native environments. Application Deadline:   This position will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants. Niyam is an Equal Opportunity (“EEO”) Employer. All qualified applicants will receive consideration without regard to race, color, creed, religion, sex, sexual orientation, gender identity, pregnancy, marital status, partnership status, age, citizenship status, veteran or military status, medical condition, genetic information, national origin, disability, unemployment status or any other characteristic prohibited by federal, state and/or local laws.    If you require a reasonable accommodation due to a disability to complete your application, or if you face challenges using our online application system and need an alternative way to apply, please reach out to us at +1 703.429.2450 or email hr@niyamit.com.

Do you want to build a scalable platform that makes qualitative insights fast, easy and valuable together with an international Tech team with competent and experienced colleagues? And are you proficient in GoLang or Python? Then this role as Backend Engineer is perfect for you. Who are we? GetWhy is the end-to-end AI-powered human insights platform, with the mission to bring companies and customers closer together and root empathy into the decision-making process. We’re a global insights provider, that with empathy at our core, redefines the boundaries of qualitive research and emphasizes the value of being truly consumer-centric. For the past decade, we’ve strived to put more empathy into the world by bridging the gap between companies and consumers, to create a better understanding and cater to human experiences on a global scale. More than that, GetWhy is a dynamic scale-up with a can-do attitude, located in the heart of Copenhagen, and rapidly growing to meet the demands of our international pool of clients. To fuel our journey, we just raised a $34.5m Series A investment from PeakSpan Capital, the largest round outside Life Science in Denmark. What does the work consist of? We are looking for a coworker who can further develop our platform together with your colleagues in Engineering and Product. You will be utilizing your coding skills in a crossfunctional team of Backend Developers, Data Scientists, Frontend Developers and an Infrastructure Engineer. You will be reporting to our Lead Engineer, who have extensive experience building insights-focused solutions and platforms. Our tech stack consists of AWS, GoLang, PHP Laravel, SQL, Mongodb, Vue/nuxt, Javascript as well as Docker and Container orchestration. What qualifications do you need? We are looking for a person who is motivated to be part of a growth journey with an ambitious and enthusiastic Engineering Team. We expect you to: Have some experience working with backend development in GoLang. Have a broad interest in web technologies in general Have motivation for working in a scaling and fast-paced environment Be as strong believer in teamwork Be professionally proficient in English Live in, or in proximity of, Copenhagen What we Offer A product that you believe in, where we truly help our customers get meaningful and valuable insights from their customers. A vibrant and international work environment with 50 colleagues across 15+ nationalities. A flexible work schedule with opportunities to work from home. A scale-up company with the ambition and potential to become a global leader Personal learning and development. We want to set the groundwork to skyrocket your career, our company is built upon everyone’s ideas and desire to progress. Competitive compensation plans along with pension A newly renovated pet-friendly office in the heart of Copenhagen, with a stunning waterfront view, relaxation areas, free daily energizers, vitamin shots, snacks, drinks, and a healthy and flexible lunch. Annual company retreats and lots of Friday bar events. We work hard, we party hard. A full-time position at Sundkaj 125, 2150 Copenhagen with a starting date as soon as you can. ...and of course, a great team spirit! How do I apply? The only thing you have to do is click on the Easy Apply button and upload your resumé or LinkedIn profile. Do we see a potential match, we will invite you in for a talk in our office to get to know you even better and to tell you more about our company, culture and the role. So, what are you waiting for? Hit the apply button and let us take the first step in our journey together. All the best, GetWhy

We are expanding our B2B consultant network with AI & Machine Learning Engineers (Python, Cloud, Git, RAG, MLOps) experienced in machine learning, applied AI solutions, and intelligent system development. Engagements may involve model development, AI integration into existing systems, optimization of ML pipelines, and applied generative AI (GenAI) use cases. If you are an independent AI professional open to flexible project-based work, we would be glad to stay connected. We are continuously building our network of experienced B2B consultants and contractors across various technology domains. By applying to this talent pool, you are expressing interest in future project-based collaborations with our team. When a suitable opportunity aligned with your expertise becomes available, we will reach out to discuss potential engagement. Please note:   These opportunities are exclusively for independent consultants and B2B contractors (freelance / company-to-company cooperation), and full-time employment roles. If you are open to flexible, project-driven collaboration, we would be glad to stay connected.

About Us: KinaTrax's mission is to provide professional and collegiate teams with game-changing insights about their most valuable asset: their athletes. We deliver research-grade markerless motion capture technology that allows teams to collect in-game biomechanical performance data on their athletes. KinaTrax camera systems are currently deployed in over 50 stadiums & labs across MLB , MiLB , & NCAA organizations - and expanding rapidly. Our comprehensive data capture & analysis tools are operationalized for daily use by players, GM's, coaches, trainers, medical staff, and beyond. As the market leader in Major League Baseball, KinaTrax has established itself as a foundational part of the teams' day to day strategy and decision-making machine. But we aren't finished. We are constantly innovating and looking to recruit talented teammates to help us continue to revolutionize this space. Your contributions will focus on bringing the next generation of athlete performance data, across a variety of sports, to teams worldwide. What we are looking for: Currently we are seeking a highly motivated candidate to lead the development of our core technologies that measure athletic performance in competitive environments. You will serve a central role in building and refining our computer vision and machine learning algorithms focused on tracking athlete movement and biomechanics. Joining at our early stage of growth will enable you to become a key figure as we seek to expand our software development team & capabilities. Ambitious individuals will have the opportunity to assume a central leadership role as the team develops, working alongside the core leadership team to define the future product roadmap and contribute to the overall company strategy. Our Stack Machine Learning: Proficient in Python, TensorFlow, PyTorch Computer Vision: Highly skilled in C++, OpenCV DevOps: Visual Studio, Github, Bazel, CMake, FFMPEG User Interface: Qt Roles & Responsibilities The primary responsibility of this role is to lead the day to day development & productizing of our Computer Vision/Machine Learning technologies from conception to deployment Own the development of system requirements and product architectures Adapt and improve existing algorithms for new environments, activities & applications Lead the recruitment & expansion of our ML/CV and software development teams Provide technical leadership and guidance to both your team members and your project peers Operate strategically and tactically. Working closely with senior leadership to define and execute the long-term strategies and product roadmaps and help set direction while staying on top of the day to day software development. Key Qualifications PhD/MS degree in Computer Vision, Machine Learning or related field with research publications; alternatively equivalent years of industry experience solving problems which do not have readily available solutions. Expertise in 3D computer vision, machine learning, and artificial intelligence Experience maintaining and promoting best practices for software development, for example: test driven design/development, unit tests, code coverage, refactoring, gated checkins, code reviews, continuous integration etc. Expertise in at least one of these specific areas: 2D/3D object(s) detection and tracking, keypoint detection, human pose recognition, image sequence/ visual/LIDAR-based tracking, and multi-object tracking, optimization, computational geometry Track record of driving research projects from start to completion, including conception, problem definition, experimentation, iteration, and finally publication or productization Demonstrated experience in recruiting and managing technical teams, including performance management. What will set you apart 5-7+ years industry or applied experience Experience deploying cloud computing solutions Container eco-systems (Kubernetes) Creative projects evaluating humans in natural environments Demonstrable interest in sports/athletic performance/biomechanics Exposure to biomechanics Experience with embedded systems Creative sensor fusion projects/solutions Experience with pose2image translation

Are you ready to take your Machine Learning skills to the next level? We are hiring a Founding Machine Learning Engineer to own the intelligence layer of our products. You will design, build, and improve the models and decision systems behind recommendations, ranking, personalisation, retrieval, agent behaviour, and selected predictive analytics use cases. You will work directly with the founders to turn ambiguous product ideas into production systems that create measurable customer value. In a team of our size, this is an end-to-end role: you may touch data exploration, modelling, evaluation, experimentation, and production iteration in the same week. This is not a pure research role. We care about people who can move from data and hypotheses to shipped systems and business impact. All you need is: Strong foundations in machine learning, statistics, computer science, or a similar quantitative discipline; Experience building and shipping ML systems or intelligent product features in production or near-production environments; Strong Python skills and comfort working across data, modelling, evaluation, and production collaboration; Good understanding of experimentation, model evaluation, feature engineering, data quality, and error analysis; Clear communication and the ability to work through messy, ambiguous product problems; High ownership, self-direction, and a strong bias toward action; 5+ years building and shipping ML systems or intelligent product features in production; Strong understanding of model evaluation, cross-validation, feature engineering, and data quality challenges in real-world environments; Experience working with large-scale behavioural, transactional, or contextual data; Strong software engineering habits, including writing clean, testable, maintainable Python code. What would be an advantage: Experience with recommendation systems, ranking, search, personalisation, or marketplace/feed optimisation; Experience with LLM applications, RAG, GenAI agents, prompt iteration, or evaluation of GenAI systems; Experience running A/B tests or online experiments; Experience working closely with product teams and translating user problems into ML solutions; Experience with real-time ML, streaming features, low-latency inference, or online learning; Experience with causal inference, uplift modelling, multi-armed bandits, or other decision-optimisation methods; Familiarity with cloud ML infrastructure, containerised deployment, and MLOps workflows; Experience in iGaming, fintech, e-commerce, or another domain with large-scale transactional and behavioural data; Experience with predictive analytics use cases such as segmentation, churn prevention, LTV modelling, or opportunity prioritisation. Your daily adventures will look like: Design, build, and improve ML systems for recommendations, ranking, personalisation, retrieval, and GenAI workflows; Turn product goals into concrete ML problems, evaluation plans, experiments, and shipped features; Work with behavioural, transactional, contextual, and unstructured data to identify signals and improve model quality; Build offline evaluation frameworks and online experiments to measure relevance, quality, latency, cost, and business impact; Improve GenAI agent behaviour through better retrieval, context management, prompting, tool use, orchestration, and evaluation; Investigate failure modes, run error analysis, and make practical tradeoffs across quality, reliability, speed, and complexity; Partner closely with platform and backend engineers to deploy, monitor, and iterate on models in production; Help define how the company does ML: metrics, experimentation discipline, technical standards, and long-term direction; Work with real-time behavioural and transactional signals to improve recommendations, personalisation, and intelligent product behaviour; Contribute to predictive and insight-driven ML use cases such as segmentation, churn prediction, recommendation measurement, and opportunity ranking; Write clean, testable Python and contribute reusable ML components and shared libraries used across the platform. What Success Looks Like in the First 6 Months: You ship meaningful improvements to a recommendation, personalisation, or GenAI workflow used in production; You establish a practical evaluation framework for one or more core ML systems; You turn ambiguous product opportunities into clear experiments and sound technical decisions; You improve at least one metric that matters, such as relevance, task completion, conversion, retention, latency, or cost efficiency; You become a trusted owner who spots high-leverage ML opportunities and drives them forward without needing detailed instruction; You help establish a repeatable approach to experimentation, model iteration, and production-quality ML development. And this is how our interview process goes: A 30-minute interview with a member of our HR team to get to know you and your experience; A 1-hour technical interview; A final interview to gauge your fit with our culture and working style. Sounds interesting? Do not hesitate to apply or contact us if you have any questions!

We are looking for a Machine Leaning Engineer (MLE) to design, build, and optimize our machine learning operation. You will play a crucial role in scaling AI models from research to production, ensuring smooth model deployment, monitoring, and lifecycle management across our Google Cloud Platform (GCP) infrastructure. You'll work closely with data scientists, ML Ops, and data engineers to automate workflows, improve model performance, and ensure reliability for our AI that serves millions of players worldwide. What You'll Do Design, develop, and deploy machine learning models and solutions, leveraging tools such as LangGraph and MLflow for orchestration and lifecycle management. Collaborate on building and maintaining scalable data and feature pipeline infrastructure for real time and batch processing using tools like BigQuery, BigTable, Dataflow, Composer(Airflow), PubSub, and Cloud Run to support ML model training and inference. Develop and implement robust strategies for model monitoring and observability to detect model drift, bias, and performance degradation, leveraging tools like Vertex AI Model Monitoring and custom dashboards. Optimize ML model inference performance to improve latency and cost-efficiency of AI applications. Ensure the overall reliability, performance, and scalability of the ML models and data infrastructure platform, including proactive identification and resolution of issues related to model performance and data quality. Troubleshoot and resolve complex issues impacting ML models, data pipelines, and production AI system. Ensure AI/ML models and workflows meet data governance, security, and compliance requirements, specifically for real-money gaming. What We're Looking For 1+ years of experience as an ML Engineer, with a focus on developing and deploying machine learning models in production environments. Strong experience in Google Cloud Platform (GCP), including services relevant to ML and data infrastructure such as BigQuery, Dataflow, Vertex AI, Cloud Run, and Pub/Sub and Composer (Airflow). Solid grasp of containerization (Docker, Kubernetes) and experience with Kubernetes orchestration platforms like GKE for deploying ML services. Experience building and deploying scalable data pipelines and machine learning models in production environments. Understanding of model monitoring, logging, and observability best practices for ML models and applications. Experience in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Familiarity with AI orchestration concepts using tools like LangGraph or LangChain is a bonus. Bonus experience includes working in gaming, real-time fraud detection, or AI personalization systems and Agentic workflows.

ABOUT SOFASCORE Sofascore is a sports-tech company created with one goal in mind – giving sports enthusiasts a deeper understanding of the game. Our platform is the leading provider of advanced sports insights. From the biggest derbies to amateur matches, every game counts - that’s why we have the largest data coverage with 20,000+ tournaments across 25 sports. This comes easy with Torneo, our very own tournament management software for lower leagues. The global recognition of the Sofascore Rating, along with the Player of the Season award for the highest-rated players, positioned us as the authority in evaluating player performance. The Sofascore team counts more than 300 experts in 20 teams, primarily playing at our home court in Croatia, but we also have talents showing their skills worldwide. More about the company /// More about the platform ABOUT THE ROLE At Sofascore, we are passionate about redefining the sports experience for millions of users worldwide through cutting-edge technology. We are looking for an experienced Senior Machine Learning Engineer to join our AI Team. This is a key role for someone eager to apply advanced ML techniques in production at scale, contributing to projects that directly impact our users: recommender systems, personalized feeds, sentiment analysis, and semantic search. As a Senior ML Engineer, you will be responsible not only for developing state-of-the-art models but also for ensuring their robustness, scalability, and seamless deployment into production environments. You will work closely with other engineers, data scientists, and product teams to deliver intelligent, reliable, and high-performing solutions. Your responsibilities Design, build, and deploy production-ready ML systems with high reliability and scalability. Develop, evaluate, and optimize recommender systems, feed systems, and NLP-driven solutions (e.g., sentiment analysis, semantic search). Implement robust MLOps practices using Docker, Kubernetes, and cloud platforms. Collaborate on system design, data pipelines, and end-to-end lifecycle management of ML models. Write clean, maintainable, and high-quality code following engineering best practices. Mentor and support less experienced colleagues, setting standards for technical excellence within the team. What we are looking for 5+ years of industry experience working on machine learning projects, with a proven track record of delivering models into production. Strong knowledge of recommender systems, NLP methods, and modern ML architectures (including transformers and LLMs). Proficiency in Python (or similar), with excellent coding and debugging skills. Hands-on experience with containerization, orchestration, and deployment technologies (Docker, Kubernetes). Strong understanding of MLOps, CI/CD pipelines, monitoring, and model lifecycle management. Ability to design scalable solutions, from prototype to production. Strong problem-solving mindset, curiosity, and passion for innovation. What sets you apart Experience working on large-scale consumer-facing ML systems. Familiarity with cutting-edge LLM applications and their integration into production systems. Research background, evidenced by impactful contributions, publications, or applied innovations. Experience mentoring and providing technical leadership in a collaborative team environment. What we offer The opportunity to work with a cutting-edge sports platform impacting millions worldwide Family benefits package Education - internal and through international conferences and workshops Top quality equipment and budget for Mobile phone Paid package of general physical examination once a year Sofascore Canteen (lunch options) Sofascore Bar (coffee and drinks on us) Numerous other benefits that we would verbally communicate to you

Sitero is a next-generation clinical trial solutions partner, working with more than 200 Pharmaceutical, Biotech, and Institutional Research Organizations globally. We use technology to drive safety, compliance, quality, and efficiency in research and clinical trials, helping customers bring life-changing treatments to market safer and faster. Driven by a mission to advance clinical research through a technology-enabled delivery model, Sitero leverages a deep well of knowledge across Technology, Clinical Operations, Biosafety, and Drug Safety. We are looking for engineers who are versatile, display leadership qualities, and are enthusiastic to take on new problems across the full stack as we continue to push technology forward. Job Title: Software Engineer, Machine Learning Location: Ontario, Canada (Mississauga area preferred) Why Sitero? At Sitero, you will work on projects that have a direct impact on human health. We offer a fast-paced environment where you can switch teams and projects as our business grows. We are an equal opportunity workplace committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. About the Role We are seeking a Software Engineer, Machine Learning to lead the development of next-generation AI technologies that power our clinical trial platforms. In this role, you will sit at the intersection of AI infrastructure, model optimization, and large-scale software engineering. You will be responsible for building scalable data pipelines for AI, optimizing model performance on hardware accelerators, and deploying state-of-the-art Large Language Models (LLMs) and Multi-Modal models that revolutionize how clinical data is processed and analyzed. You will join a collaborative team that addresses unique problems focused on maximizing scientific and real-world impact. You will not only build AI but also embody the future of engineering by using AI to accelerate your own workflows—leveraging the very tools we build and integrate to augment software development practices. Responsibilities AI & ML Infrastructure: Design, develop, and deploy large-scale ML infrastructure and state-of-the-art AI solutions (e.g., LLMs, Multi-Modal models) to enhance clinical trial efficiency and safety. Systems Architecture: Serve as a technical leader for the design of high-throughput, low-latency data pipelines and storage solutions (data loading, caching, vector stores) required for massive datasets. AI-Augmented Engineering: Actively utilize and advocate for AI-assisted development tools (e.g., coding assistants, automated refactoring agents) to accelerate the software development lifecycle, generate boilerplate, write tests, and optimize legacy codebases. Cross-Functional Leadership: Collaborate with clinical operations, product, and research teams to translate complex business requirements into robust engineering solutions. Establish alignment on technical strategy and influence the direction of Sitero’s AI platforms. Mentorship: Mentor engineers across the organization, fostering best practices in distributed systems, ML engineering, and the adoption of AI-native development workflows. Minimum Qualifications Bachelor's degree in computer science, Engineering, or a related technical field, or equivalent practical experience. 8+ years of experience in software development with proficiency in Python, C++, or similar languages. 7+ years of experience leading technical project strategy and optimizing industry-scale ML infrastructure (model deployment, evaluation, fine-tuning). 2+ years of experience with state-of-the-art AI techniques (LLMs, RAG, Computer Vision) and frameworks (PyTorch, TensorFlow, JAX). Demonstrated proficiency in using AI models to augment software engineering practices—specifically using LLMs for code generation, debugging, architectural design validation, and documentation. Experience designing and scaling data infrastructure, including distributed storage systems, data lakes, or high-performance ETL frameworks. Preferred Qualifications Master’s degree or PhD in Computer Science, Artificial Intelligence, or related technical field. Experience with hardware/software to co-design and optimize models for specific accelerators (GPUs, custom silicon). Background in the Life Sciences, Pharmaceutical, or Clinical Research industries. Familiarity with modern data storage formats (Parquet, ORC) and vector database technologies. Proven track record of technical leadership in a complex, matrixed organization, managing cross-functional projects.

Join FxPro : a leading international fintech company. Be a part of our expanding international team, with offices in Limassol, London, Nassau, Dubai, and Yerevan. FxPro boasts a diverse workforce of over 600 employees representing 50 nationalities, making it an exciting and dynamic workplace. At   FxPro , we see each team member as an integral part of our success story. As a Machine Learning Engineer you will develop new ML models and collaborate with the Dealing, Business Analysts and our BI Experts. Responsibilities: Develop and deploy machine learning models for trader profiling and other business applications Collect, clean, and preprocess large datasets, applying feature engineering and transformation techniques Monitor model performance in production and retrain models as needed Integrate models into operational systems, ensuring scalability, security, and optimal performance Collaborate with cross-functional teams to align model development with business needs Continuously optimize models and stay current with the latest advancements in ML research Requirements: Bachelor’s degree in Mathematics, Statistics, Computer Science, Engineering, Economics, or a related field, or equivalent practical experience 3+ years of experience in machine learning, with a proven track record of developing and deploying models in production Strong understanding of mathematical statistics and probability theory Proficiency in Python and machine learning libraries (e.g., pandas, NumPy, Scikit-Learn) Solid understanding of machine learning algorithms (e.g., regression, classification, clustering) Familiarity with MLOps tools (e.g., Airflow, MLflow, DVC), containerization (e.g., Docker), and orchestration (e.g., Kubernetes) Experience with SQL, including complex queries, analytic functions, and querying large datasets. Familiarity with model monitoring and logging tools (e.g., Prometheus, Grafana) Knowledge of cloud platforms (Azure, AWS, GCP) Experience with version control and CI/CD practices (e.g., Git, Jenkins) Strong problem-solving and analytical skills A self-driven attitude with a strong sense of ownership and the ability to work independently to solve problems and develop solutions Excellent command of the English language, both written and verbal Interview steps Recruiter screen   (30 minutes) Technical Interview   (1 hour) Test task Technical Interview (1 hour) Final Interview   with manager (30 minutes) Our benefits Excellent compensation package Medical insurance Provident fund In-house gym with a personal trainer Free daily lunch catering, snacks, and beverages Company discount card for various products & services 21 days of a nnual leave and 10 days of sick leave annually Shuttle bus service from Limassol Birthday gift Relocation bonus and visa/work permit support

📍 San Francisco | Work Directly with CEO & founding team | Report to CEO | OpenAI for Physics | 🏢 5 Days Onsite Machine Learning Infrastructure Engineer Location: Onsite in San Francisco Compensation: Competitive Salary + Equity Who We Are UniversalAGI is building OpenAI for Physics. AI startup based in San Francisco and backed by Elad Gil (#1 Solo VC), Eric Schmidt (former Google CEO), Prith Banerjee (ANSYS CTO), Ion Stoica (Databricks Founder), Jared Kushner (former Senior Advisor to the President), David Patterson (Turing Award Winner), and Luis Videgaray (former Foreign and Finance Minister of Mexico). We're building foundation AI models for physics that enable end-to-end industrial automation from initial design through optimization, validation, and production. We're building a high-velocity team of relentless researchers and engineers that will define the next generation of AI for industrial engineering. If you're passionate about AI, physics, or the future of industrial innovation, we want to hear from you. About the Role UniversalAGI is hiring an Infrastructure Engineer to build and own the execution platform powering our research and customer deployments: data generation + simulation orchestration + training/fine-tuning infrastructure + benchmarking pipelines + production deployments in customer environments. You’ll work closely with the CEO and founding team to turn research into repeatable, scalable, reliable systems - internally and in customer infrastructure. This is a “ship outcomes” role: your work directly determines how fast we can iterate, how reproducible our results are, and how reliably we deliver in production. What You’ll Do Build the foundation platform (internal) Build and operate scalable infrastructure for data generation and simulation workflows (job orchestration, scheduling, queues, retries, observability). Build reproducible pipelines for training/fine-tuning and benchmarking (artifact/version management, experiment tracking, dataset lineage). Own cost/performance tradeoffs across compute, storage, networking, and runtime efficiency. Deploy to customers (external) Lead deployments of our stack into customer cloud/on-prem environments, including secure networking, permissions, and data movement. Build robust deployment patterns: environment provisioning, CI/CD, rollbacks, monitoring, and incident response. Partner with customers to ensure reliability and repeatability under real-world constraints (security, compliance, infra limits, data governance). Qualifications Strong software engineering skills (clean code, debugging, reliability, reproducibility). Hands-on experience building/operating infrastructure for ML/compute-heavy workflows: pipelines, job orchestration, GPU compute, storage, CI/CD, monitoring. Olympic athlete mindset : You have high standards for yourself and are obsessed with measurable improvement on the metrics you are delivering to customers. Resourcefulness : you know when to do the “quick & correct” fix vs. when to invest in a robust solution, and you can justify the tradeoff with impact/ Ownership : Comfortable owning work end-to-end and being accountable for measurable outcomes. Bonus Qualifications Experience with workflow orchestration (e.g., Ray, Kubernetes, Slurm). Experience with GPU infrastructure and distributed training systems. Experience building evaluation/benchmarking frameworks with strong reproducibility guarantees. Experience deploying into regulated / security-sensitive environments (gov/defense/enterprise). Experience with simulation/HPC pipelines (CFD, meshing, batch workloads) is a plus but not required. Experience in an FDE-style / delivery execution role (or similar “ship results fast” environments). Cultural Fit Technical Respect : Ability to earn respect through hands-on technical contribution Intensity : Thrives in our unusually intense culture - willing to grind when needed Customer Obsession : Passionate about solving real customer problems, not just publishing papers Deep Work : Values long, uninterrupted periods of focused work over meetings High Availability : Ready to be deeply involved whenever critical issues arise Communication : Can translate complex model decisions to customers and team Growth Mindset : Embraces the compounding returns of intelligence and continuous learning Startup Mindset : Comfortable with ambiguity, rapid change, and wearing multiple hats Work Ethic : Willing to put in the extra hours when needed to hit critical milestones Team Player : Collaborative approach with low ego and high accountability Bias for Action : Ships experiments fast, learns from failures, and iterates quickly What We Offer Opportunity to define the future of physics AI from the ground up Work on cutting-edge problems at the intersection of deep learning and physics simulation Direct collaboration with the founder & CEO and ability to influence company strategy Competitive compensation with significant equity upside In-person first culture - 5 days a week in office with a team that values face-to-face collaboration Access to world-class investors and advisors in the AI space Benefits We provide great benefits, including: Competitive compensation and equity. Competitive health, dental, vision benefits paid by the company. 401(k) plan offering. Flexible vacation. Team Building & Fun Activities. Great scope, ownership and impact. AI tools stipend. Monthly commute stipend. Monthly wellness / fitness stipend. Daily office lunch & dinner covered by the company. Immigration support. How We’re Different “The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again... who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly." - Teddy Roosevelt At our core, we believe in being “in the arena. ” We are builders, problem solvers, and risk-takers who show up every day ready to put in the work: to sweat, to struggle, and to push past our limits. We know that real progress comes with missteps, iteration, and resilience. We embrace that journey fully knowing that daring greatly is the only way to create something truly meaningful. If you're ready to train the models that will revolutionize physics simulation, push the boundaries of what AI can learn, and deliver real impact, UniversalAGI is the place for you.

About DeepWalk DeepWalk is a fast-growing venture that helps cities keep people safe using computer vision to map and monitor their sidewalks. We have ongoing contracts with cities, universities, and engineering firms. In the past year, we’ve processed thousands of miles of sidewalk across 20+ states , generating millions of labeled data points used in real-world infrastructure decisions. We’ve raised $4.1M, recently closed a $2.1M seed round led by Enable Ventures, and we currently generate 7-figure revenue helping communities across America. Staff Software Engineer — Computer Vision Platform $150,000 – $190,000 base salary + equity · Hybrid (Chicago) We’re hiring a Staff Software Engineer to lead the technical direction of DeepWalk’s computer vision platform for automated sidewalk inspection. This role is focused on tackling high-impact, open-ended problems around large-scale imagery, production ML systems, and data pipelines that handle hundreds of terabytes of data. We’re specifically looking for engineers who have built and operated computer vision or ML systems in production at scale (not just research or prototyping). You’ll play a key role in how we process, analyze, and deliver insights from millions of images, supporting cities as they work to build safer, more accessible infrastructure. In this role, you’ll work closely with a handful of experienced engineers, help set technical direction, and establish patterns the team can scale with. Our core stack today includes Python-based CV/ML systems running on AWS, with data pipelines and services designed to handle large volumes of imagery and geospatial data. What You’ll Do As a Staff Engineer at DeepWalk, you’ll have significant ownership over both the systems we build and how we build them. You will… Own the lifecycle of our computer vision models, including training, evaluation, deployment, and iteration Improve model performance in real-world conditions (noise, edge cases, data drift) Design and improve data pipelines that process thousands of miles of sidewalks and millions of images Lead architectural decisions for handling hundreds of terabytes of geospatial and visual data, including storage layout, pipeline reliability, and inference performance Set and evolve best practices around deployment, observability, system reliability, and scalability Act as a senior individual contributor and mentor, helping raise the technical bar across the engineering team Work closely with the CEO and operations team to turn business needs into clear technical priorities What Success Looks Like (First 3–6 Months) Build a strong understanding of our current computer vision systems and data pipelines Identify the biggest scaling, reliability, and maintainability gaps as we grow Propose and begin executing on a clear plan to improve how we deploy, monitor, and operate production ML systems Become a trusted technical partner to leadership What We’re Looking For Required 6+ years of experience, including direct ownership of production ML or computer vision systems (training, deployment, and ongoing operation) Experience taking models from training → production → monitoring → iteration in a real-world environment Experience owning system architecture and influencing technical decisions across teams Experience deploying computer vision or ML systems in real-world production environments Fluency in at least one modern backend language (Python, Java, TypeScript, Go, etc.) Strong understanding of system design, scalability, and distributed systems Experience with cloud platforms such as AWS, GCP, or Azure Comfort working in a startup or growth-stage environment with changing requirements Nice to Have Experience with large-scale or real-time data pipelines Infrastructure-as-Code experience (Terraform, CDK, etc.) ML observability, data validation, or model deployment experience Familiarity with geospatial, imagery, or sensor-driven data An interest in urban planning, accessibility, or civic technology Compensation & Benefits $150,000 – $190,000 base salary, based on scope and experience Equity Unlimited PTO (most of our team takes 3–4 weeks per year) Health insurance 401(k) with ~4% match (100% on first 3%, 50% on the next 2%) Convenient office location in The Loop Hybrid work environment (typically 2 days in-office) DeepWalk participates in the federal E-Verify program to confirm the employment eligibility of all newly hired employees. DeepWalk is an Equal Opportunity Employer and is committed to building a diverse and inclusive workplace. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other legally protected status. In accordance with applicable laws, DeepWalk provides equal pay for equal work and complies with all federal, state, and local pay transparency and compensation requirements.

About DeepWalk DeepWalk is a fast-growing venture that helps cities keep people safe using computer vision to map and monitor their sidewalks. We have ongoing contracts with cities, universities, and engineering firms. In the past year, we’ve processed thousands of miles of sidewalk across 20+ states , generating millions of labeled data points used in real-world infrastructure decisions. We’ve raised $4.1M, recently closed a $2.1M seed round led by Enable Ventures, and we currently generate 7-figure revenue helping communities across America. Senior/Staff Software Engineer — Computer Vision Platform $150,000 – $190,000 base salary + equity · Hybrid (Chicago) We’re hiring a Senior/Staff Software Engineer to lead the technical direction of DeepWalk’s computer vision platform for automated sidewalk inspection. This role is focused on tackling high-impact, open-ended problems around large-scale imagery, production ML systems, and data pipelines that handle hundreds of terabytes of data. We’re specifically looking for engineers who have built and operated ML or computer vision systems in production at scale (not just research or prototyping). You’ll play a key role in how we process, analyze, and deliver insights from millions of images, supporting cities as they work to build safer, more accessible infrastructure. In this role, you’ll work closely with a handful of experienced engineers, help set technical direction, and establish patterns the team can scale with. Our core stack today includes Python-based CV/ML systems running on AWS, with data pipelines and services designed to handle large volumes of imagery and geospatial data. What You’ll Do As a Staff Engineer at DeepWalk, you’ll have significant ownership over both the systems we build and how we build them. You will… Own the lifecycle of our computer vision models, including training, evaluation, deployment, and iteration Improve model performance in real-world conditions (noise, edge cases, data drift) Design and improve data pipelines that process thousands of miles of sidewalks and millions of images Lead architectural decisions for handling hundreds of terabytes of geospatial and visual data, including storage layout, pipeline reliability, and inference performance Set and evolve best practices around deployment, observability, system reliability, and scalability Act as a senior individual contributor and mentor, helping raise the technical bar across the engineering team Work closely with the CEO and operations team to turn business needs into clear technical priorities What Success Looks Like (First 3–6 Months) Build a strong understanding of our current computer vision systems and data pipelines Identify the biggest scaling, reliability, and maintainability gaps as we grow Propose and begin executing on a clear plan to improve how we deploy, monitor, and operate production ML systems Become a trusted technical partner to leadership What We’re Looking For Required 5+ years of experience, including direct ownership of production ML or computer vision systems (training, deployment, and ongoing operation) Experience taking models from training → production → monitoring → iteration in a real-world environment Experience owning system architecture and influencing technical decisions across teams Experience deploying ML systems in real-world production environments Fluency in at least one modern backend language (Python, Java, TypeScript, Go, etc.) Strong understanding of system design, scalability, and distributed systems Experience with cloud platforms such as AWS, GCP, or Azure Comfort working in a startup or growth-stage environment with changing requirements Nice to Have Experience with large-scale or real-time data pipelines Infrastructure-as-Code experience (Terraform, CDK, etc.) ML observability, data validation, or model deployment experience Familiarity with geospatial, imagery, or sensor-driven data An interest in urban planning, accessibility, or civic technology Compensation & Benefits $150,000 – $190,000 base salary, based on scope and experience Equity Unlimited PTO (most of our team takes 3–4 weeks per year) Health insurance 401(k) with ~4% match (100% on first 3%, 50% on the next 2%) Convenient office location in The Loop Hybrid work environment (typically 2 days in-office) DeepWalk participates in the federal E-Verify program to confirm the employment eligibility of all newly hired employees. DeepWalk is an Equal Opportunity Employer and is committed to building a diverse and inclusive workplace. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, veteran status, or any other legally protected status. In accordance with applicable laws, DeepWalk provides equal pay for equal work and complies with all federal, state, and local pay transparency and compensation requirements.

Help us push the boundaries of what's possible in LLM post-training. If you love training models, exploring new architectures, running experiments, and turning research insights into products that ship, we'd love to meet you. About Inference.net Inference.net trains and hosts specialized language models for companies who want frontier-quality AI at a fraction of the cost. The models we train match GPT-5 accuracy but are smaller, faster, and up to 90% cheaper. Our platform handles everything end-to-end: distillation, training, evaluation, and planet-scale hosting. We are a well-funded ten-person team of engineers who work in-person in downtown San Francisco on difficult, high-impact engineering problems. Everyone on the team has been writing code for over 10 years, and has founded and run their own software companies. We are high-agency, adaptable, and collaborative. We value creativity alongside technical prowess and humility. We work hard, and deeply enjoy the work that we do. Most of us are in the office 4 days a week in SF; hybrid works for Bay Area candidates. About the Role You will be responsible for conducting research into experimental models, training systems, and modalities to create novel products for our customers. Your work will span from exploring new architectures and learning methods to optimizing latency and efficiency, with the goal of delivering better models to customers. Your north star is pushing the frontier of what's possible in LLM post-training. You'll explore new techniques, run rigorous experiments, and when something works, help bring it into production with the help of your teammates. This includes training models for customers and running evaluations as part of validating your research. This role reports directly to the founding team. You'll have the autonomy, a large compute budget / GPU reservation, and technical support to explore ambitious ideas and ship the ones that work. Key Responsibilities Research and experiment with new model architectures to improve quality, efficiency, or capability Explore methods to decrease inference latency and improve serving efficiency Run experiments with new learning methods, including novel approaches to SFT, RLHF, DPO, and other post-training techniques Perform reinforcement learning research to improve model alignment and capability Develop and improve our distillation pipeline for training high-quality models from frontier teachers Train models for clients and run evaluations to validate research findings in production settings Create robust benchmarks and evaluation frameworks that ensure custom models match or exceed frontier performance Stay current with ML research and identify techniques that can improve our platform Collaborate with applied engineers to bring successful research into production systems Document findings and share knowledge with the team Requirements 3+ years of experience training AI models using PyTorch Deep understanding of transformer architectures, attention mechanisms, and model internals Hands-on experience with post-training LLMs using SFT, RLHF, DPO, or other alignment techniques Experience with LLM-specific training frameworks (e.g., Hugging Face Transformers, DeepSpeed, Megatron, TRL, or similar) Strong experimental methodology, including ability to design, run, and analyze rigorous experiments Track record of implementing ideas from recent ML papers Experience training on NVIDIA GPUs at scale Strong foundation in ML fundamentals: optimization, loss functions, regularization, generalization Nice-to-Have Publications in ML venues Experience with model distillation or knowledge transfer Experience with LLM speed optimization techniques Familiarity with vision encoders, multimodal models, or other modalities Experience with distributed training and infrastructure at scale Contributions to open-source ML projects You don't need to tick every box. Curiosity and the ability to learn quickly matter more. Compensation We offer competitive compensation, equity in a high-growth startup, and comprehensive benefits. The base salary range for this role is $250,000 - $350,000, plus equity and benefits, depending on experience. Equal Opportunity Inference.net is an equal opportunity employer. We welcome applicants from all backgrounds and don't discriminate based on race, color, religion, gender, sexual orientation, national origin, genetics, disability, age, or veteran status. If you're excited about pushing the boundaries of custom AI research, we'd love to hear from you. Please send your resume and GitHub to amar@inference.net and/or here on Ashby.

About Lightfield Lightfield is an AI-native CRM that assembles itself from your email, calendar, and meetings. It captures every interaction and turns it into organized context: accounts, tasks, follow-ups, and insights, so nothing slips through the cracks. We’re rethinking CRM from first principles. Instead of forcing teams to maintain rigid systems, Lightfield learns from how companies actually work, adapting, automating, and surfacing the insight that drives growth. We’re building the CRM platform we always wished existed: fast, intelligent, and genuinely helpful. We are backed by Greylock, Lightspeed, and Coatue, and our founders previously built Tome, a generative AI presentation product used by over 25 million people. Before Lightfield, our team worked on Llama, Instagram, Facebook Messenger, Pinterest, Google, and Salesforce. About the role Lightfield's AI/ML team builds the experiences at the core of our product, developing new applications to wow our customers. Today, the team is focused on building a powerful, domain-specific AI that outperforms generic LLMs We’re inspired by the challenge of creating innovative new AI products for people doing serious work, and we’re looking to grow our AI/ML team to meet that challenge. What you'll do Create and ship magical, highly-differentiated AI experiences that sales teams can’t live without Craft Lightfield AI/ML strategy in tight collaboration with founders and execs Pioneer the training of new models that leverage both historical data and synthetic training data Prototype innovative, LLM-powered experiences, and drive their development into robust product features Help build a world class AI/ML engineering team by recruiting and mentoring teammates Who you are You have 5+ years of industry experience in NLP, with a strong portfolio of model training You have a strong understanding of deep learning AI/ML frameworks or cloud services You have hands-on ML Ops experience You possess deep expertise in NLP and model training, specifically with LLMs Proven experience adapting open-source generative models for specific use cases, demonstrating a deep understanding of its architecture and capabilities Bonus Points Proven track record of leading successful AI/ML research projects in a product environment Publications in applied AI/ML scientific journals Experience navigating open source/vendor solutions in LLM ops space (Langchain, Llama, Pinecone, etc) Benefits & Perks Competitive salary Meaningful early equity Health insurance (medical, dental, vision) 3 weeks of PTO 11 paid company holidays + we enjoy a winter holiday break 3 months of paid family leave Wednesdays work from home Regular team dinners, events, offsites, and retreats 401k plan Other perks include: commuter and lunch stipend

The Bot Company We're building a helpful robot for every home. We're a small team of engineers, designers, and operators based in San Francisco. Our team comes from Tesla, Cruise, OpenAI, Google, Pixar, and many other great companies. In the past we've shipped to hundreds of millions of users and know what it takes to build amazing products and experiences. Our team is deliberately lean to promote rapid decision making and do away with bureaucracy and hierarchy. Everyone is an IC and is empowered with massive scope, radical ownership, and direct responsibility. We work across the stack with a culture built for rapid iteration and fast execution. What we look for in all candidates All roles at The Bot Company demand extreme sharpness and the ability to move fast in high-intensity environments. Throughout the process, we expect candidates to demonstrate: Exceptional mental acuity: you think quickly, learn instantly, and reason across unfamiliar domains. Engineering curiosity: you naturally dig into how systems work, even outside your specialty. High performance mindset: you move fast, handle ambiguity, and excel when the environment is demanding. Machine Learning: Whole-Body Control We are building high-performance whole-body controllers that produce robust, agile motion and manipulation in the real world. You will train low-level control policies in simulation and own the stack from environment design to large-scale training and sim-to-real deployment. What You'll Do Train whole-body policies for locomotion, manipulation, and coordinated motion. Build scalable simulation environments in IsaacLab, MuJoCo, or similar with parallel rollouts. Design rewards and curricula that enable stable long-horizon learning. Own sim-to-real transfer using domain randomization and structured evaluation. Run and debug large-scale GPU training experiments. Requirements Very strong coding skills in Python, C++, or Rust. CUDA is a plus. Strong foundations in modern reinforcement learning. Experience training embodied control policies. Hands-on robotics simulation experience. Solid intuition for dynamics, contact, and actuation. Comfortable running large-scale GPU experiments. Why Join You’ll work with a small, elite team on challenges that require speed, intelligence, and deep engineering instinct. If you enjoy understanding systems at all levels, move fast, and think even faster, you’ll thrive here.

About Alljoined Alljoined aims to solve the communication bottleneck between humans and technology by decoding thoughts from the brain, entirely non-invasively. We apply deep learning research to large scale EEG datasets collected on affordable hardware to decode images, text, and video initially, and eventually moving to internal thought. We are state-of-the art in capabilities and are fully vertically integrated. Our goal is to develop a general consumer interface to completely transform what we can do at home and work. We are actively growing our world-class team of researchers to build the next interface to improve individual lives as well as the well-being of society as a whole. About the Role We’re seeking a talented Machine Learning Researcher to join our core R&D team. This role involves designing and implementing advanced machine learning models for EEG-based neural decoding, publishing high-impact research, and developing the core infrastructure for our brain decoding systems. You will work closely with leading experts in neural decoding and AI, pushing the boundaries of what’s possible in brain computer interfaces. Key Responsibilities Research & Model Development: Develop, train, and refine state-of-the-art deep learning models for neural decoding, building on the latest advancements in ML architectures (e.g., transformers, diffusion models, etc). Explore novel approaches for modeling high-frequency timeseries EEG datasets along with a number of adjacent data modalities. Translate research insights into production-grade code that integrates seamlessly with our in-house BCI stack. Collaboration & Publication: Collaborate with a team of neuroscientists and ML engineers to create scalable, end-to-end neural decoding solutions. Publish findings at top-tier ML and AI conferences (e.g., NeurIPS, ICML, ICLR, CVPR) and contribute to open-source communities where appropriate. Qualifications Educational Background & Experience: Bachelor’s degree in Computer Science or a related domain (e.g., AI, Computational Neuroscience, Mathematics, Biomedical Engineering, etc), with 5-7 years of experience in ML research or applied ML engineering; OR Graduate degree (M.S., Ph.D.) in Computer Science or a related domain (e.g., AI, Computational Neuroscience, Biomedical Engineering) with 3+ years of experience in ML research or applied ML engineering. Candidates with a Ph.D. and/or experience in high profile ML research labs are strongly preferred. Technical Expertise: Multimodal Representation Learning (CLIP-style contrastive objectives, masked autoencoding) Generative Modeling (diffusion, transformer-decoders, latent-GANs) Temporal Sequence Modeling (state-space models, STFT-aware transformers, RWKV) A track record of high-quality research demonstrated by publications in top ML conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR). Strong proficiency in Python and PyTorch, familiarity with ML tooling, and distributed training. Experience working in a production-quality codebase with modern code review standards. Compensation Range $140,000 - $250,000/year + equity While this represents our expected range based on market data, final compensation will be determined based on your specific qualifications and may be outside this range. Benefits Options for housing support Visa sponsorship 3% 401k matching Health insurance

About Us: At Quartermaster AI, we believe the ocean should be a safe and sustainably managed resource for all. By leveraging cutting-edge AI and robotics, we unlock capabilities that were only recently impossible. Our distributed open-ocean systems enable every vessel to sense, compute, and communicate, enhancing maritime domain awareness for those who need it most. Job Description: Quartermaster AI is seeking a Senior AI/ML Engineer with an emphasis in RF analysis to develop and deploy machine learning systems that utilize RF data for real-time maritime intelligence. You’ll work in a small team of experienced engineers to build detection, classification, and tagging models that help provide contextual understanding of vessel activity based on observed RF signatures. Key Responsibilities: Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking. Build and maintain dataset curation pipelines, including AIS-correlated ground truth labeling, synthetic RF data generation, and augmentation strategies for class-imbalanced maritime environments. Build the interface between DSP feature outputs and model inputs by defining pre-processing, normalization, and feature extraction requirements in coordination with the DSP engineer. Develop model evaluation frameworks and benchmarking harnesses; define quantitative performance criteria and drive iterative improvement against them. Optimize models and inference workflows for deployment on edge compute hardware. Document model architecture, training methodology, dataset provenance, and validation results. Qualifications (Preferred): Master's or PhD in Machine Learning, Signal Processing, or a closely related field — or equivalent demonstrated experience. 5+ years building and deploying ML systems with a focus on RF or signals data. Proficiency in Python and deep learning frameworks; familiarity with RF-native tooling such as Torchsig is a strong plus. Strong understanding of signal alignment, temporal synchronization, and feature extraction from IQ and spectral data. Proven ability to ship production models, not just research prototypes. Experience in maritime, aerospace, or operationally demanding spectral environments. Experience building labeled RF datasets from ground truth sources. Familiarity with edge inference constraints and optimization techniques (quantization, pruning, model distillation). Active Secret clearance or demonstrated ability to obtain one.

About us Our mission is to reinvent the way people learn, starting with language. Learning a language can change a life by opening doors to new cultures, careers, and communities. Two billion people around the world are actively trying to learn a language, but the best way to learn (one-on-one tutoring) is hard to access at scale and hasn’t been meaningfully improved in decades. Speak is building a human-level, AI-powered tutor in your pocket: a conversation-first experience that lets learners actually speak, get instant feedback, and progress through carefully designed lessons. The result is a complete path from beginner to confident speaker across multiple languages. Speak first launched in South Korea in 2019, where Speak has now become the number one language learning app, and we now serve learners across many markets and 15+ languages. Speak is one of the world’s leading AI companies, with over $150m raised in venture investment from OpenAI, Accel, Founders Fund, Khosla Ventures, and more, with a distributed team across San Francisco, Seoul, Tokyo, Taipei, and Ljubljana. About this role We’re hiring an ML Engineer, Assessments to help build best-in-class assessment systems across multiple products (Speak for Business, B2C, and new surfaces). You will work in a tight loop with our Assessment Design Lead (Content/Learning Design) , Machine Learning, Product, and Engineering to turn assessment constructs and rubrics into reliable, scalable scoring + feedback systems. This role owns the implementation, deployment, and ongoing quality of our assessment algorithms and ML systems. While there is immediate need to improve and expand production assessments, this work is also building a platform capability that can be reused across the app. What you’ll be doing Ship and own assessment ML systems end-to-end Build, deploy, and maintain scoring models/pipelines (feature extraction → model training → inference → feedback generation) Own monitoring, regression tests, and ongoing iteration to maintain accuracy targets Define and operationalize evaluation Implement validation/evaluation frameworks for assessments, including metrics, test sets, and offline/online analysis Translate assessment requirements into measurable acceptance criteria and guardrails Partner deeply with the Assessment Design Lead Co-develop the strategy, together with the Content team, to grow assessments into a core platform at Speak Work in a tight weekly loop to deliver incremental improvement Drive near-term delivery across products Stand up or improve summative assessments (spoken language ability) and bring them reliably to production Prototype and validate formative assessment approaches to measure improvement over weeks/months Support data and labeling strategy Help define data needs for training/evaluation (including psychometric measurement needs) Build or improve pipelines that support label collection and analysis (especially for efficacy studies) What we’re looking for Domain expertise in spoken language proficiency assessment (linguistics, applied linguistics, pedagogy, or equivalent experience) Strong experience designing and running evaluation + validation for assessment/scoring systems, and tailoring approaches to a specific product use case 4+ years building automatic proficiency assessment systems (or equivalent depth in closely related scoring/evaluation domains) PhD is helpful but not required Proven ability to ship ML models to production (not only research), including reliability, monitoring, and iteration Strong generalist ML/analysis skills (statistics, Python, PyTorch/model training) Ability to operate cross-functionally and communicate clearly with non-technical partners (Content/LD, PM, leadership) Nice to have Experience with speech/audio ML Experience with psychometrics concepts (reliability/validity, calibration) How we work (collaboration expectations) This role is designed to be highly collaborative with the Assessment Design Lead. Success depends on a tight loop where constructs/rubrics and model outputs co-evolve — not a sequential handoff. Why work at Speak Join a fantastic, tight-knit team at the right time:  we're growing very quickly, we've most recently raised our Series C from some of the top investors in the valley, and we've achieved product-market fit in our initial markets. You'd join at a magical time when a single person could significantly change the course of the company. Do your life's work with people you’ll love working with:  we care strongly about our craft and want every person at Speak to feel like they're growing every day. We believe in the idea that working with people you both enjoy and have respect for makes everything better. We hire thoughtfully and only work with people we admire deeply. Global in nature:  We're live in over 40 countries and launching in a number of new markets soon. We have dedicated offices in San Francisco, Ljubljana, Seoul, and Tokyo, and you’ll have the opportunity to talk to users in each of these regions on a regular basis as well as travel. Impact people's lives in a major way:  Learning a language is one of the single most life-changing skills one can learn, and right now 99% of people never achieve their goal because the process is broken. We’re helping millions of people achieve their goals and improve their lives. Speak does not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.

Senior Machine Learning Engineer Remote US & Canada We are seeking a highly motivated and autonomous Senior Machine Learning Engineer to join our team. In this role, you will be a driving force behind our core ML models and underlying data and training pipelines. We are looking for a self-starter, someone who is internally driven, passionate about their craft, and capable of collaboratively identifying, prioritizing, and executing workflows with their peers and stakeholders. If you thrive in an environment where you are trusted to own your projects end-to-end and can seamlessly explain complex technical concepts to both technical and non-technical stakeholders, we want to hear from you. We expect you to: Have deep passion for video games and think on behalf of our players Be curious, a problem solver and a self starter who is comfortable taking risks and proactively identifying and pursuing business needs. Know how and when to apply your knowledge, and be willing to share it with those around you Be comfortable with ambiguity and able to navigate it to iteratively refine the problem space Be responsible for your domain of expertise on projects from inception to delivery and beyond Required Skills 5+ years of experience in Machine Learning, Data Engineering, or a related quantitative field, with a track record of delivering end-to-end ML products Expert knowledge of statistical modeling and ML methods including Classical ML, Deep Learning, NLP, and Anomaly Detection, with the ability to select the right tools for the task Demonstrated proficiency in experimental design, hypothesis testing, and evaluating model performance Proven track record of building and deploying scalable ML solutions in production, ideally using Kubernetes and GCP Strong proficiency in Python, Java, or Scala, with an emphasis on writing clean, maintainable, and production-ready code, alongside the ability to write highly optimized SQL for complex dataset construction Mastery of optimized SQL and experience with big data processing frameworks (e.g., Spark, Beam) and modern data lakehouse formats (e.g., BigQuery, Snowflake, Iceberg, Parquet) Exceptional ability to translate complex technical architectures and ML concepts for non-technical stakeholders Preferred Experience Any of the following would be highly preferred, but most of all, we value team players who are eager to learn and contribute: • Experience with graph databases, collaborative filtering, or user vectorization • Experience with large-scale sentiment analysis • Experience with Typescript or Golang • Experience with source control, CICD, Protobuf, or infrastructure as code • Experience in the video game industry • Experience in a small company with a startup feel • Interest in technical writing and sharing your work Perks: • Paid Time Off, Holidays and Two Weeks Winter Break • Employees and their dependents get medical, dental, and vision coverage, regardless of their level, tenure, or position within the company. Moreover, these benefits start on the first day of the job—there’s no waiting period before they kick in. • Pet Insurance for those who need it too. • Compassionate leave for employees who needs to take care of their family members • Pre-tax wellness stipend • Pre-tax work from home stipend • Access our savings plan (401K program) with company match • Mental health resources including Headspace membership and Employee Assistance Program (EAP) • Discount portal for everyday goods and services • Employee inclusive and diversity initiatives such as Grow Together • Support for personal professional development We look forward to meeting you! Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment Visa at this time. The salary range for this position is $120,000 USD to $205,700 USD annually, with the opportunity to earn an annual discretionary bonus. This salary range is an estimate, and the actual salary may vary based on the Company’s compensation practices. Employees in this position are eligible to participate in the Company’s standard employee benefit programs, which currently include the following: medical, dental, vision, 401k, and paid time off. Our base pay is benchmarked against regional market and industry data and is adjusted to reflect the cost of living in your specific geographic area. LI-Remote

In this role you will help scale and optimize our training systems and core model code. You’ll own critical infrastructure for large-scale training, from managing GPU/TPU compute and job orchestration to building reusable and efficient JAX training pipelines. You’ll work closely with researchers and model engineers to translate ideas into experiments—and those experiments into production training runs. This is a hands-on, high-leverage role at the intersection of ML, software engineering, and scalable infrastructure. The Team The ML Infrastructure team supports and accelerates PI’s core modeling efforts by building the systems that make large-scale training reliable, reproducible, and fast. The team works closely with research, data, and platform engineers to ensure models can scale from prototype to production-grade training runs. In This Role You Will - Own training/inference infrastructure: Design, implement, and maintain systems for large-scale model training, including scheduling, job management, checkpointing, and metrics/logging. - Scale distributed training: Work with researchers to scale JAX-based training across TPU and GPU clusters with minimal friction. - Optimize performance: Profile and improve memory usage, device utilization, throughput, and distributed synchronization. - Enable rapid iteration: Build abstractions for launching, monitoring, debugging, and reproducing experiments. - Manage compute resources: Ensure efficient allocation and utilization of cloud-based GPU/TPU compute while controlling cost. - Partner with researchers: Translate research needs into infra capabilities and guide best practices for training at scale. - Contribute to core training code: Evolve JAX model and training code to support new architectures, modalities, and evaluation metrics. What We Hope You’ll Bring - Strong software engineering fundamentals and experience building ML training infrastructure or internal platforms. - Hands-on large-scale training experience in JAX (preferred), PyTorch. - Familiarity with distributed training, multi-host setups, data loaders, and evaluation pipelines. - Experience managing training workloads on cloud platforms (e.g., SLURM, Kubernetes, GCP TPU/GKE, AWS). - Ability to debug and optimize performance bottlenecks across the training stack. - Strong cross-functional communication and ownership mindset. Bonus Points If You Have - Deep ML systems background (e.g., training compilers, runtime optimization, custom kernels). - Experience operating close to hardware (GPU/TPU performance tuning). - Background in robotics, multimodal models, or large-scale foundation models. - Experience designing abstractions that balance researcher flexibility with system reliability.

About TensorWave Our mission is simple: deliver seamless, secure, reliable, and resilient AI compute at scale. We've built a versatile cloud platform that eliminates infrastructure barriers, empowering builders to focus on innovation instead of fighting their stack. Because breakthrough AI should move at the speed of ideas, not infrastructure. About the Role We’re looking for a Senior Machine Learning Engineer to join our team during an exciting phase of growth. In this role, you’ll be responsible for building and operating the core systems that power large-scale ML training and inference across TensorWave’s GPU platform , working closely with cross-functional partners to support business objectives while upholding our standards for excellence, collaboration, and impact. What You’ll Do Design, operate, and improve ML infrastructure systems supporting distributed training and inference workloads Build reliable, repeatable workload execution and orchestration patterns across shared GPU environments Troubleshoot performance, reliability, and scalability issues across the ML stack Partner with ML, systems, and platform teams to improve developer experience and operational efficiency Who You Are Required Qualifications Bachelor of Science in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience Expertise supporting production ML systems using SLURM and Kubernetes Strong understanding of GPU-accelerated workloads and distributed systems concepts Solid Linux fundamentals and experience debugging infrastructure-level issues Ability to build automation and tooling - Python, Go, etc. Preferred Qualifications Experience working across schedulers, orchestration platforms, or cluster managers Familiarity with large-scale GPU environments or HPC-style systems Experience improving infrastructure reliability, utilization, or performance at scale What We Offer Stock Options 100% paid Medical, Dental, and Vision insurance for Employees Company Health Savings Account Contributions 100% paid Short Term and Long Term Disability Insurance for Employees Life and Voluntary Supplemental Insurance Options Other Insurance Options, such as Pet & Legal Insurance Various Supplementary Health Benefits, such as discounted Virtual Healthcare Appointments and Serious Illness Support Flexible Spending Account 401(k) Employee Assistance Program Flexible PTO Paid Holidays Parental Leave Other In-Office Perks Equal Employment Opportunity TensorWave is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of any protected status under applicable law. Reasonable Accommodations TensorWave provides reasonable accommodations in accordance with applicable laws. If you require accommodation during the hiring process, please contact accomodations@tensorwave.com. Employment Eligibility All offers of employment are contingent upon verification of identity and authorization to work in the United States, as required by law. Background Checks Where permitted by law, employment may be contingent upon the successful completion of a job-related background check. Data Privacy Notice By submitting an application, you acknowledge that TensorWave may collect, use, and retain your personal information for recruiting and employment-related purposes in accordance with applicable data privacy laws.

Senior/Staff Machine Learning Data Scientist In Brief We’re an early-stage startup on a mission to make healthcare proactive by empowering physicians, nurses, and care team members with real-time data to save lives. TLDR : Independent end-to-end model development with ability to work cross-functionally with Clinical, Engineering, and Product to clarify and prioritize specifications and features for our life-saving AI models. Who We Are Bayesian Health’s mission is to improve patient outcomes by empowering clinicians with the insights they need to make the right decision for the right patient at the point-of-care. We’re a diverse team of clinicians, engineers, machine learning experts, product designers, and performance improvement leaders committed to enabling smarter, patient-specific care delivery through unlocking the power of data. We’re funded by top tier tech and biotech investors: Andreessen Horowitz, American Medical Association’s venture arm, Catalio Partners, and LifeForce Capital. Our company has won many awards; most recent recognitions include: Forbes AI Top 50, World Economic Forum Tech Pioneer, Time Best Inventions, BioTech AI Company of the Year. Read more about our recent publication in Nature Medicine that associates our products with lives saved. What you’ll do As a Senior/Staff Machine Learning Data Scientist, you are not satisfied with training and tuning ML models that predict clinical conditions in patients; you also want to own the effectiveness of your model in the real world. In practice, that means you aren’t afraid to get your hands dirty by writing data mapping code, debugging a specific patient case by following patient data as it moves through our AWS services, or improving the timeliness of your model’s predictions by reading and writing production-grade Python and SQL code. Responsibilities Model Prototyping: Develop and tune innovative, new ML models and labeler systems based on deep understanding of clinical use cases and state-of-the-art ML methods Productionizing: The same models that you develop with production-grade Python Deploying: Identify strategies for improving our production ML-based systems, and write, debug, and deploy production-grade Python code to implement those strategies Cross-Functional Alignment : Data Science for storytelling – understand model performance and metrics, and present this to technical and non-technical users, both internally and externally Minimum qualifications Ph.D. in a relevant field plus 3+ years experience shipping ML based software products Experience owning your ML models from prototyping to production, especially real-time algorithms that update dynamically across time Experience writing production-grade Python and SQL code to implement and evaluate ML models in production systems Track record of using statistics and performance metrics to compare end-to-end ML and product performance Preferred qualifications Experience shipping breakthrough or 0-1 products from end to end, interpreting and leveraging State-of-the-Art methods to do so Experience using messy clinical and health data to design new products for large Health Systems Experience with any of the following: PyTorch, PySpark, HL7, FHIR, EHR, time series data, signal processing, MLFlow, anomaly detection, Bayesian statistics, quantile regression, time-series forecasting You bring passion and enthusiasm to your work, and are excited to join a growing team to Get Stuff Done and save lives! Bayesian Health provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

About Mindbeam We are building the next-generation AI infrastructure for open source and enterprise. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level. What drives us is not only advancing technology, but empowering the people behind it. We are a community of researchers, engineers, and visionaries who believe that collaboration, curiosity, and openness fuel progress. If you’re motivated by impact and inspired to build tools that others can build upon, you’ll be in the right place. Mission Design and optimize large-scale pre-training systems that power Mindbeam’s generative AI models. Role Expectations • Build scalable pre-training pipelines for foundation models, optimizing throughput and efficiency. • Implement distributed training strategies across GPUs/TPUs and high-performance clusters. • Collaborate with researchers to translate experimental setups into production-ready workflows. • Develop monitoring and fault-tolerance systems to ensure reliable large-scale training. • Continuously benchmark and tune performance across hardware and software stacks. Background • Bachelor’s, Master’s, or PhD in Computer Science, Engineering, or related field—or equivalent experience. • 2+ years of experience with large-scale model training and distributed systems. • Strong coding skills in Python and familiarity with ML frameworks (PyTorch, TensorFlow, JAX). • Experience with GPU scheduling, memory optimization, and parallelism strategies. • Comfort with containerized and orchestrated environments (Docker/Kubernetes). • Understanding of high-performance computing and networking bottlenecks. About You You thrive on scale and complexity. You enjoy solving system-level bottlenecks, pushing hardware and software to their limits, and working closely with researchers to accelerate cutting-edge AI development.

About Mindbeam We are building the next-generation AI infrastructure for both open-source and enterprise applications. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level. Mission Advance AI performance and efficiency by engineering systems for fine-tuning, evaluation, and deployment at scale. Role Expectations • Develop pipelines for post-training tasks such as fine-tuning, evaluation, and model compression. • Implement scalable systems for model deployment, monitoring, and optimization. • Collaborate with researchers to validate experimental results in production contexts. • Build tools to automate benchmarking and regression testing. • Identify opportunities to improve efficiency in resource utilization and inference speed. Background • Bachelor’s, Master’s, or PhD in Computer Science, ML/AI, or related field—or equivalent practical experience. • 2+ years of experience in model training, evaluation, or deployment. • Strong skills in Python, ML frameworks (PyTorch/TensorFlow), and data pipeline tools. • Familiarity with optimization techniques (quantization, pruning, distillation). • Hands-on experience deploying models on cloud and/or GPU infrastructure. • Knowledge of monitoring and observability tools. About You You combine deep technical expertise with a pragmatic mindset. You thrive on bridging research and production, and you’re motivated by the challenge of making cutting-edge models usable and efficient at scale.

About Mindbeam We are building the next-generation AI infrastructure for open source and enterprise. Our work is deeply research-oriented and passionate about developing ground-breaking innovations to take state-of-the-art AI applications to the next level. Mission Push the boundaries of performance by developing custom kernels and low-level optimizations for next-generation AI workloads. Role Expectations • Design and implement custom GPU/accelerator kernels to maximize performance. • Profile, benchmark, and optimize critical ML workloads. • Collaborate with researchers to translate algorithmic advances into efficient, production-ready code. • Stay current with hardware advancements (CUDA, ROCm, TPU) to inform kernel design. • Document and share best practices for low-level optimization. Background • Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, or related field—or equivalent experience. • 2+ years of experience in GPU programming, parallel computing, or systems-level optimization. • Strong coding skills in C++, CUDA, or similar languages. • Familiarity with ML frameworks and their low-level backends. • Experience optimizing workloads for distributed and heterogeneous compute environments. • Comfort with profiling tools and performance diagnostics. About You You are detail-oriented, performance-obsessed, and excited by the challenge of squeezing out every ounce of compute efficiency. You enjoy working at the intersection of algorithms and hardware, and you thrive in a collaborative environment where bold ideas are encouraged.

About FINNY FINNY is a growth platform for financial advisors. We are on a mission to make great financial advice easier to find. Today, access to quality financial guidance is limited, not because advisors don’t exist, but because the right connections are hard to make when it matters. We’re fixing that with AI-powered tools that help advisors find, engage, and retain the clients they can genuinely help. We've raised a $4.5M Seed from Y-Combinator in S24 and now $17M Series A led by Venrock. We work with over 1,000 firms across the wealth management ecosystem, and have been recognized as the leader in fintech innovation—winning #1 at the Morningstar Fintech Showcase, #1 at 2025 Wealthies, and being featured across the industry. We’re based in Chelsea, NYC, building fast and ambitious systems at the intersection of data, AI, and real-world wealth services. About the Team Being a Machine Learning Engineer at FINNY means owning the models that power search, matching, ranking, recommendations, data and intelligent automation across the product. This is a model first role. While you’ll work with real production data and pipelines, your primary impact comes from designing, training, evaluating, and improving ML systems that directly shape user outcomes. You’ll partner closely with product, backend, and frontend teams to turn ambiguous problems into measurable model improvements. What You’ll Do Help build FINNY’s core models Design, train, and iterate on custom models that power data imputation, prospect and audience recommendations, campaign customization and personalization, and automations. Build & improve models in production Take models from research → experimentation → deployment → iteration Own offline evaluation, online metrics, and feedback loops Improve model performance over time through better objectives, features, and training strategies—not just more data Advanced modeling & experimentation Apply and adapt techniques such as: Fine-tuning RL methods (DPO) Transfer learning and weak supervision Synthetic data generation and augmentation Operate effectively in low-signal, noisy, or cold-start environments Contribute to ML systems & infrastructure Work with backend engineers to productionize models reliably and at scale Help define standards for model versioning, evaluation, deployment, and monitoring Influence long-term ML strategy and reduce technical debt in modeling workflows What We’re Looking For You’re a model builder at heart You care deeply about how models learn, not just how pipelines run You’re comfortable reasoning about loss functions, tradeoffs, and evaluation You enjoy designing solutions when the problem is underspecified and data is imperfect You’re strong technically Very strong Python with extensive hands-on experience building ML systems. Strong statistical and mathematical foundations. Proven experience training, fine-tuning, and deploying custom models into production, not just experimentation or offline research Experience designing loss functions, evaluation metrics, and validation strategies aligned with real-world product objectives Familiarity with model lifecycle management: versioning, reproducibility, monitoring, and iteration in production environments You’ve shipped ML systems before You’ve taken models beyond notebooks and into real products You understand failure modes, monitoring, and iteration in production ML Startup experience Your working style You tackle ambiguity head-on and turn fuzzy problems into concrete experiments You move fast, iterate, and aren’t precious about first approaches You communicate clearly about model behavior, limitations, and tradeoffs In-person, NYC (5 days/week in Chelsea office) Compensation & Benefits FINNY offers a competitive compensation package including: Competitive salary and equity Medical, dental, and vision insurance Flexible paid time off 401(k) Food and meals provided in our NYC office Team offsites and events Equal Opportunity Employer FINNY is an equal opportunity employer. We are committed to diversity and inclusion in our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

About the Role We're looking for founding Machine Learning Engineers (MLEs) to own and improve our core action models end-to-end - the intelligence that powers Composite's proactive automation platform. You'll work at the intersection of LLM inference, browser understanding, and low-latency systems, shipping models that need to feel instant while reasoning over complex page state and user context. Unlike hosted browser solutions that introduce latency and auth barriers, or consumer-focused "AI browsers," we run AI directly through professionals' existing browsers via a Chrome extension, creating instant response times with zero migration or IT friction. This architecture creates unique ML challenges. This is a high-ownership role on our small, exceptional team where your work ships directly to users and has the potential to tangibly improve the work lives of hundreds of millions of people. About Composite College-educated professionals spend 85% of their day as digital factory workers in Chrome, clicking through repetitive browser tasks. Composite is building the proactive layer for productivity so professionals around the world can focus on meaningful, high-leverage work. We're training action prediction models that run in real time, anticipating what you'll do next based on page context and prior interactions. We've raised $5.6M in seed funding led by Nat Friedman and Daniel Gross, with participation from Menlo Ventures, Anthropic's Anthology Fund, SVAngel, and other incredible investors. What You'll Work On Improve the accuracy and latency of our core models across diverse web applications to predict users' intended next actions and execute them faster than manual input Design and optimize LLM inference pipelines, including token caching strategies, streaming architectures, and network-level optimizations between client and server Build evaluation frameworks and data pipelines to measure and improve model quality at scale Experiment with retrieval-augmented approaches using vector databases for contextual memory Develop synthetic data generation pipelines for browser interaction training data Work with DOM states, accessibility trees, and user interaction data to improve browser understanding Ship features end-to-end that go directly to users — this is not a research-only role What We're Looking For ML & Systems Strong ML fundamentals with hands-on experience training and deploying models in production Obsessive about latency — experience optimizing inference pipelines to feel instant to end users Deep care about data quality, with the instinct to build tooling that ensures it Experience with LLMs, transformer architectures, or sequence prediction problems Comfortable working across the stack — our system spans a Chrome extension, Electron app, Cloudflare Workers edge proxy, and inference providers Core Qualities Character: You're someone we'd want to work closely with for the next ten years. You approach challenges with curiosity rather than ego. You're a team player, a great communicator, and aren't afraid to be wrong. Work Ethic: You're energized by hard problems and comfortable working intensely toward ambitious goals. Raw Intelligence: You can quickly understand complex systems and solve novel, ambiguous problems with self-guidance. Bonus Experience with browser automation, Chrome extensions, or web scraping at scale Familiarity with accessibility tree / DOM parsing for page understanding Background in RL or online learning from user interaction data Experience with vector databases (e.g., Turbopuffer, Pinecone) and hybrid search Full-stack development experience (TypeScript, Node.js, React) Our Values Disagree and commit: Respectfully challenge decisions you disagree with, even when it's uncomfortable. Don't censor yourself or your ideas. Once a decision is determined, everyone commits wholly. Clear and consistent standards: Decisions are made based on a shared framework that applies for everyone. We don't leave room for "rules for you, not for me" or any perceived hypocrisy. Over-communicate: Nothing slows down a company more than confusion, mis-, or under-communication. Leave no room for ambiguity. Ask dumb questions. Write things down clearly. Health is #1: Stay hydrated. Eat a balanced diet. Sleep 8 hours a night. Exercise frequently. Maintain good social and mental health. Not doing so affects your mood and long-term productivity. Do the right thing.

Weave is looking for engineers hungry for fun challenges who can join our self-empowered teams and contribute in both technical and non-technical ways. You will be joining a team of talented developers that share a common interest in distributed backend systems, data, scalability, and continued development. You will get a chance to apply these, and other skills, to new and ongoing projects to make machine learning more approachable, data more available, and easier to discover and use by helping design how teams build out AI powered features at Weave. Our teams are cross-functional agile teams composed of a product owner, backend and frontend devs and devops. Teams are highly autonomous with the ownership and ability to act in Weave’s best interest. Above all, your work will impact the way our customers experience Weave while working closely with a highly skilled team to accomplish varying goals and cultivate our phenomenal culture. PURPOSE The Machine Learning Team's mission is to enable product innovation by making it painless for developers to build ai powered applications that require access to large sets of data. Machine learning is challenging but we are striving to democratize access to the tools and technology that powers it so teams can build cutting edge features safely and responsibly without a PhD in Data Science. As a Machine Learning Engineer on the team you’ll be building models for new products with emerging technologies, at scale. We handle data for hundreds of millions of people daily. This position will be available for fully remote in the US with an opportunity to work in an office, if located near the Lehi, UT Headquarters. Reports to: Engineering Director What You Will Own Design and Develop machine learning infrastructure, tooling, and models to help teams deliver world class experiences. Help product and development teams understand the data lifecycle and the inherent experimental nature of machine learning. Build internal products and platforms to enable teams to incorporate AI into their features and customer facing products. Consult with teams to help them understand common patterns, anti-patterns, and tradeoffs of machine learning. Guide them through creating excellent customer experiences end to end. Build scalable, resilient services to support data integration, event processing, and platform extensions. Contribute to the continued evolution of product functionality that services large amounts of data and traffic. Write code that is high-quality, performant, sustainable, and testable while holding yourself accountable for the quality of the code you produce. Coach and collaborate inside and outside the team. You enjoy working closely with others - helping them grow by sharing expertise and encouraging best practices. Work in a cloud environment, considering the implementation of functionality through several distributed components and services. Work with our stakeholders to translate product goals into actionable engineering plans. What You Will Need to Accomplish the Job High integrity, team-focused approach, and collaboration skills to build tight-knit relationships across Weave with various roles and stakeholders. Responsive person with a strong bias for action. 5+ years of experience in any structured back-end language, i.e. Go, Java or Python (Go and Python experience is a plus). Experience moving and storing TBs of data or 100M’s to 10B’s of records. Experience building and deploying ML driven B2B multi-tenant applications in production environments. Experience with common ML technologies such as Python, Jupyter, Workflow Engines (Dagster, MLFlow, KubeFlow, etc), DVC, Triton Server, LLMs, Postgres, and others. Experience with modern ML tools and techniques such as LLMs, RAG, Prompt Engineering, Fine Tuning, multi-modal models, and others. Experience with data labelling or annotation for audio or text use cases. Understanding of distributed systems and building scalable, redundant, and observable services. Expertise in designing and architecting systems for distributed data sets and services. Experience building solutions to run on one or more of the public clouds (e.g., AWS, GCP, etc.). Experience providing stable well designed libraries and SDKs for internal use. Self driven and a thirst for learning in a quickly changing industry. Demonstrated track record of delivering complex projects on time and have experience working in enterprise-grade production environments. Strategic thinker with a strong technical aptitude and a passion for execution. What Will Make Us Love You A background with data analysis, visualization, and presentation. 3+ years of experience in data science, machine learning, or predictive analytics in addition to engineering experience. Experience with natural language models, embeddings, and inference in production, at scale. Experience with real-time audio models and voice use cases such as transcription, ASR pipelines with interruption detection, audio alignment, and speech synthesis. Experience with emerging technologies such as Model Context Protocol (MCP). Proficient understanding of containers, orchestrators, and usage patterns at scale including networking, storage, service meshes, and multi-cluster communication. Experience with Kubernetes or GKE and the Operator Pattern (GCP), specifically, a plus. Experience with highly sensitive data such as PHI (HIPAA) and PII data. Experience with automation and container based workflow engines. Experience with GitOps, IaC, and configuration driven systems. A preference for open source solutions. A track record of clean abstractions and simple to use APIs. Deep understanding of distributed data technologies such as streaming, data mesh, data lakes, warehouses, or distributed machine learning. A desire to advance the state of the art with new and innovative technologies. Enjoys working in a greenfield environment using rapid prototyping. Enjoys working with open-ended, evolving problems, and domains. LI-DNI Employment with Weave is contingent upon the successful completion of a background check , conducted in accordance with applicable laws. At Weave, we use Artificial Intelligence (AI) tools to help us work more efficiently and create a smoother candidate experience. AI may assist with things like writing job descriptions, scheduling interviews, or reviewing applications against job-related criteria. For additional information, please review the External AI Policy Statement available on our Careers page. Weave is an equal opportunity employer that is committed to fostering an inclusive workplace where all individuals are valued and supported. We welcome anyone who is hungry to learn, problem-solve and progress regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other applicable legally protected characteristics. If you have a disability or special need that requires accommodation, please let us know. Beware of recruitment fraud . All official correspondence will occur through Weave branded email. We will never ask you to share bank account information, cash a check from us, or purchase software or equipment as part of your interview or hiring process.

Why Achira At Achira, we are building a team of world-class scientists, ML researchers, and engineers to work together to move beyond the beaten path in drug discovery. We are actively exploring the next frontier of model architectures for AI x Chemistry: developing world models for the physical microcosm. Our goal is to make biology at the molecular level something that can be learned, predicted, and designed. At Achira, you’ll operate at the frontier scale of massive compute, massive data, and massive ambition. You’ll own impactful work end-to-end, from ideation to architecture to deployment on distributed infrastructure. We are a well-funded, talent-dense organization that values rigor, speed, execution, and an ownership mindset. We’re looking for new members who share our sense of relentless urgency and are natural collaborators who value team success. About the Role We're looking for a rare individual who thrives at the intersection of machine learning systems architecture and distributed computing. You will help architect the future of molecular machine learning by enabling our scientific teams to flexibly conduct experiments at scale, pushing the boundaries of foundation simulation models. While we prefer candidates willing to work from our San Francisco office, highly skilled candidates may be considered for working from New York City with travel to San Francisco as needed. Both locations are offered as hybrid roles, spending at least some of your time working from the office in collaboration with coworkers. Travel is part of all roles at Achira, both to conferences and corporate on-site activities. What You’ll Do Build and maintain robust multi-stage asynchronous workflows for running data generation, training, and evaluations for our machine learning stack. Rationalize machine learning systems design and software architecture. Identify blockers and build solutions that scale to the size of foundation models. Operate as the glue between research scientists and the infrastructure team. About You At least two years relevant industry experience. Highly fluent in and enthusiastic about PyTorch and JAX. Used to thinking in asynchronous primitives. Strong views on library design: clean abstractions, minimal surface area, consistency. Solid track record of observable artifacts (e.g., GitHub) showing clear, well-documented code. ML generalist who knows what scalable, reliable ML systems look like. Nice to Have Even if you hit none of these bonus features, we encourage you to apply! Experience with equivariant architectures, geometric deep learning, or GNNs (NequIP, MACE, SchNet, PaiNN, or similar), and/or ML-assisted drug discovery. Experience building in declarative workflow orchestration frameworks like Flyte, Dagster, etc. Lack of fear around interacting with quantum chemical scientists and their data pipelines.

About Air Space Intelligence ASI's mission-critical technology powers decision-making across aviation, defense, energy, and other critical infrastructure domains. Backed by top-tier investors including Andreessen Horowitz, Spark Capital, and Renegade Partners, ASI delivers operational decision superiority—compressing days of analysis into seconds of action. ASI is leading the way and pushing the boundaries of what’s possible. What You Will Do: As part of our Defense engineering team, you will design and deploy production-grade systems that integrate machine learning models into scalable software pipelines. You’ll develop and ship features that leverage ML to solve real-world optimization and prediction problems, working with modern infrastructure like Kubernetes, AWS, and MLOps tooling. You’ll approach problems with a software engineer’s mindset—prioritizing robustness, maintainability, and performance at scale. What We Value: Proficiency in Python and experience with production ML tooling and frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Experience using LLMs in production environments — covering prompt engineering, fine-tuning, RAG systems, and frameworks like LangChain Strong understanding of data structures, algorithms, and software engineering best practices. Familiarity with classical ML, deep learning with emphasis on transformer architectures, and MLOps concepts. Experience building and maintaining scalable, reliable production ML systems with robust data pipelines, including expertise with Apache Beam, MLflow, and similar production-grade tools. Commitment to high-quality ML engineering practices, including data versioning, experiment tracking, model governance, and automated testing pipelines. A bias for simplicity and clarity in solving complex problems. Intellectual curiosity and willingness to collaborate. Clear communication and collaboration across cross-functional teams. How We Hire: We look at the interview process not as screening test but rather as an opportunity to simulate what it would look like working together. We build the interview process around you. ASI works with export controlled technology and restricted U.S. Government data, including on contracts mandating U.S. immigration status and location restrictions for performing personnel. Employment offers are contingent on ability to timely obtain all required authorizations for contemplated job duties.

Company A1 is building a proactive AI smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows. Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Role As Technical Lead, Machine Learning, you own the execution layer of A1’s intelligence. You translate research direction into reliable, scalable, production-grade ML systems. This role sits at the intersection of research, infrastructure, and product. You are responsible for making models trainable, deployable, observable, and performant under real-world constraints. What You'll Do Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. Architect and operate scalable inference systems, balancing latency, cost, and reliability. Design and maintain data systems for high-quality synthetic and real-world training data. Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. Make pragmatic trade-offs and ship improvements quickly, learning from real usage. Work under real production constraints: latency, cost, reliability, and safety Outcomes Research and models reliably translate into production-ready solutions with clear performance and quality targets. ML pipelines, training loops, and inference systems are stable, efficient, and maintainable. Production issues are detected, debugged, and resolved quickly, minimizing user impact. Team members are supported, aligned, and able to deliver high-impact ML work with minimal friction. Iterations on models and systems are measurable, safe, and improve user experience over time. Tech Stack Python PyTorch / JAX GPU-based training and inference system Ideal Experience You have built or shipped real ML systems used by people, not just demos. You are comfortable working with large models and understanding their failure modes. You write strong, production-grade code and care about system correctness. You are self-directed, pragmatic, and take full ownership of outcomes. You communicate clearly and collaborate well in small, high-trust teams. How We Work The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product Interview process If there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews. Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite. We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.

About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About the role AI and machine learning are reshaping how developers debug, monitor, and ship software, and Sentry is uniquely positioned to lead that shift. We sit on a novel and massive dataset of real production errors, spans, and logs from tens of thousands of engineering organizations — the kind of signal that makes ML genuinely useful, whether it's a clustering model that groups related issues, a ranking system that surfaces the right alert at the right time, or an agent that proposes a fix. We're looking for an Engineering Manager to lead and grow our Machine Learning Engineering team. This team owns the full spectrum of ML at Sentry: classical techniques like clustering, ranking, anomaly detection, and embeddings that quietly power core product surfaces today, alongside the LLM-based and agentic systems shaping where the product is headed. You'll partner closely with product, design, and engineering leaders to decide where ML belongs in our products, what kind of ML actually fits the problem, and how we translate that work into experiences millions of developers rely on every day. In this role you will Set technical direction across the team's full ML surface area — from classical models for clustering, ranking, and anomaly detection to LLM-based and agentic systems — and make sharp calls about which approach fits each problem Define how the team evaluates and monitors ML systems in production, from offline metrics to online experimentation to model and agent observability Stay hands-on enough to review code and model designs, contribute to architecture discussions, and unblock engineers on complex ML problems Define team roadmap and deliverables, scope work, allocate resources, and keep execution on track against ambitious goals Partner with product managers, designers, and engineering leaders across Sentry to identify the highest-impact opportunities for ML in our products Foster career growth for the engineers on your team, and recruit exceptional ML talent as the team scales You'll love this role if you Have a strong ML engineering background that you use to inform and validate the team's technical decisions, and you're comfortable staying close to the code Are excited to shape how AI and developer tools come together, and want to define how engineers and coding agents collaborate to find and fix problems Are driven by impact and want to lead a team working on high-stakes, high-visibility projects Love building things from the ground up — the ML team is still in its early chapters, and there's plenty of greenfield to shape Thrive in cross-functional environments and enjoy collaborating across product, design, and engineering to ship great work Care deeply about growing the engineers around you and finding opportunities for them to do the best work of their careers Qualifications 8+ years of professional engineering experience, with significant time spent building and shipping machine learning systems in production 3+ years of engineering management experience, ideally leading ML, AI, or data-focused teams Familiarity with deploying and operating ML models at scale, including evaluation, monitoring, and iteration in production Strong judgment in ambiguous, fast-moving environments Excellent written and verbal communication; comfortable working across product, research, and engineering A research background in machine learning, statistics, or a related field (MS, PhD, or equivalent research experience) is a plus but not a requirement Not sure if you meet 100% of the qualifications? We encourage you to apply anyway. We're interested in people who are excited about this opportunity and eager to grow. The base salary range (or hourly wage range, if applicable) that Sentry reasonably expects to pay for this position is $220,000 to $280,000. A successful candidate's actual base salary (or hourly wage) amount will be determined by a variety of relevant factors including, without limitation, the candidate's work location, education, work and other relevant experience, skills, and job-related knowledge. A successful candidate will be eligible to participate in Sentry's employee benefit plans/programs applicable to the candidate's position (including incentive compensation, equity grants, paid time off, and group health insurance coverage). See Sentry Benefits for more details about the Company's benefit plans/programs. Equal Opportunity at Sentry Sentry is committed to providing equal employment opportunities to its employees and candidates for employment regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other legally-protected characteristic. This commitment includes the provision of reasonable accommodations to employees and candidates for employment with physical or mental disabilities who require such accommodations in order to (a) perform the essential functions of their jobs, or (b) seek employment with Sentry. We strive to build a diverse team, with an inclusive culture where every teammate can thrive. Sentry is an open-source company because we believe that everyone, everywhere, should have the ability and tools to make great software. Software should be accessible. That starts with making our industry accessible. If you need assistance or an accommodation due to a disability, you may contact us at accommodations@sentry.io . Want to learn more about how Sentry handles applicant data? Get the details in our Applicant Privacy Policy .

About Sentry Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. About the role As a Staff Machine Learning Engineer on Sentry’s AI/ML team, you’ll be directly responsible for developing the models and agents used to make our product smarter and more capable. This role is crucial; you will be at the forefront of integrating AI and machine learning into our core products, from issue triage and resolution to predictive analytics for application performance monitoring. Your work will help companies around the globe gain actionable insights into their software, enabling them to build better products, faster. In this role you will Build state-of-the-art agentic AI systems to triage, debug, and solve real production issues Leverage Sentry’s novel (and massive) dataset of errors, spans, and profiles Own the development of major initiatives in the AI/ML space You'll love this job if you Are driven by impact and enjoy working on high-stakes, high-visibility projects Enjoy building things. You will have the opportunity to join the AI/ML team as one of its foundational members Thrive in cross-functional teams and enjoy building features alongside developers and product teams Qualifications Minimum 4+ years of professional experience with a MS/PhD degree in computer science, machine learning, or a related field Minimum 6+ years of professional experience with Bachelor’s degree in computer science, machine learning, or a related field Demonstrated expertise building production-grade agentic systems and tools You are comfortable writing production quality code (we use Python) Expertise with deep learning frameworks (we use PyTorch) Familiarity in deploying machine learning models at scale in production environments Experience in writing technical documentation, mentoring, and presenting to technical audiences Proven track record of owning a system, feature, or component, leading or collaborating with multiple engineers and teams The base salary range (or hourly wage range, if applicable) that Sentry reasonably expects to pay for this position is $240,000 to $300,000. A successful candidate’s actual base salary (or hourly wage) amount will be determined by a variety of relevant factors including, without limitation, the candidate’s work location, education, work and other relevant experience, skills, and job-related knowledge. A successful candidate will be eligible to participate in Sentry’s employee benefit plans/programs applicable to the candidate’s position (including incentive compensation, equity grants, paid time off, and group health insurance coverage). See Sentry Benefits for more details about the Company’s benefit plans/programs. Equal Opportunity at Sentry Sentry is committed to providing equal employment opportunities to its employees and candidates for employment regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other legally-protected characteristic. This commitment includes the provision of reasonable accommodations to employees and candidates for employment with physical or mental disabilities who require such accommodations in order to (a) perform the essential functions of their jobs, or (b) seek employment with Sentry. We strive to build a diverse team, with an inclusive culture where every teammate can thrive. Sentry is an open-source company because we believe that everyone, everywhere, should have the ability and tools to make great software. Software should be accessible. That starts with making our industry accessible. If you need assistance or an accommodation due to a disability, you may contact us at accommodations@sentry.io . Want to learn more about how Sentry handles applicant data? Get the details in our Applicant Privacy Policy .

About Sesame Sesame believes in a future where computers are lifelike - with the ability to see, hear, and collaborate with us in ways that feel natural and human. With this vision, we're designing a new kind of computer, focused on making voice agents part of our daily lives. Our team brings together founders from Oculus and Ubiquity6, alongside proven leaders from Meta, Google, and Apple, with deep expertise spanning hardware and software. Join us in shaping a future where computers truly come alive. About the Role The best platforms don't just support a workflow — they transform who can participate in it. At Sesame, we're building lifelike, compelling characters. That quality doesn't come from models alone. It comes from tight collaboration between engineers, ML researchers, and the creative professionals who define what a believable character is. This is a full-stack engineering role focused on the internal tooling and platforms that power our character research pipeline. We're looking for engineers who are excited to build the systems that enable and accelerate that collaboration. You'll architect and develop the internal platforms that take our team from raw data to fully-realized characters — evaluating quality and iterating cross-functionally through a React-based frontend atop a Python/FastAPI backend, backed by cloud ML infrastructure. The goal isn't just to support what we're doing today; it's to build a platform where our creative and research teams can experiment, iterate, and push the frontier of what's possible in character AI. Responsibilities: Design and build internal tools that let engineers, researchers, and creative professionals experiment with and assemble configurations of models — importing datasets, running training jobs, evaluating outputs — without needing to write code for every workflow Build rich evaluation interfaces that surface model quality in ways that are readable and actionable for everyone, not just engineers Collaborate closely with ML researchers and creative teams to deeply understand their workflows, then find the automation opportunities that accelerate their work Take responsibility for the reliability and evolution of the tooling platform: monitoring, iterating on user feedback, and raising the ceiling of what's possible as the team's needs grow Contribute to ML infrastructure where it connects to tooling — you'll understand the pipeline well enough to know what to expose, what to abstract, and what to automate Required Qualifications: Demonstrated 4+ years experience building and shipping production full-stack systems, with a strong sense of UI/UX for tools designed to be used by experts in other fields Experience building internal platforms or tooling, not just features — you think about the system, not just the ticket Proficiency with modern web stacks (we use React/Next.js and Python/FastAPI) Experience with cloud infrastructure (we're on GCP/Kubernetes) A strong sense of reliability and knowing when to build vs. borrow Curiosity about the domain: you don't need to be an ML engineer, but you should want to understand what you're building tools for Comfort working across disciplines — researchers, creative professionals, and operators — and translating between very different ways of thinking Sesame is committed to a workplace where everyone feels valued, respected, and empowered. We welcome all qualified applicants, embracing diversity in race, gender, identity, orientation, ability, and more. We provide reasonable accommodations for applicants with disabilities. Contact careers@sesame.com for assistance. Full-time Employee Benefits: 401 (k) max employer match: 3.5% of compensation 100% employer-paid health, vision, and dental benefits for you and your dependents Unlimited PTO and sick time Flexible spending account with employer matching up to $1,650/year (medical FSA) Guardian Employee Assistance Program (EAP) Opportunity to share in the company's success with competitive stock options Benefits do not apply to contingent/contract workers.

Shape the Future of AI & Data with Us At Datatonic, we are Google Cloud's premier partner in AI, driving transformation for world-class businesses. We push the boundaries of technology with expertise in machine learning, data engineering, and analytics on Google Cloud Platform. By partnering with us, clients future-proof their operations, unlock actionable insights, and stay ahead of the curve in a rapidly evolving world. Your Mission As a Lead Machine Learning Engineer , you will be the technical authority for our most ambitious client projects. You will set the technical vision, guide teams of talented engineers, and translate complex business challenges into cutting-edge AI solutions on Google Cloud. You'll be an advocate of high-quality engineering and best-practice in production software as well as rapid prototypes. This is a hands-on leadership role where you will not only architect solutions but also actively lead client discussions and oversee project delivery from start to finish. Your responsibilities will involve building trusted relationships with prospects, finding creative ways to use machine learning to solve problems, scoping projects, and overseeing the delivery of these engagements. To be successful, you will need strong ML & Data Science fundamentals and will know the right tools and approach for each ML use case. You'll be comfortable with model optimization and deployment tools and practices. Furthermore, you'll also need excellent communication and consulting skills, with the desire to meet real business needs and deliver innovative solutions using AI & Cloud. What You’ll Do Drive Technical Strategy from Pre-Sales to Delivery: Act as the lead technical authority in high-stakes engagements. You will partner with our commercial team to architect winning solutions, and then lead the delivery of enterprise-grade systems—such as GenAI agents for financial institutions, real-time recommendation engines for global retailers, or predictive maintenance models for the manufacturing sector. Architect & Implement Production-Grade Solutions : Own the complete technical lifecycle for your projects. You will design end-to-end ML architectures on GCP, implement robust MLOps pipelines using Infrastructure-as-Code (Terraform), and ensure all solutions—from sophisticated multi-agent systems to classical models—are optimized for performance, scalability, and security. Shape Our Technical Standards: Collaborate directly with the Head of Delivery to define the technical DNA of our ML practice, evolving the best practices, architectural patterns, and standards that ensure excellence across the company. Lead Strategic Internal Initiatives: Spearhead the development of internal accelerators and reusable frameworks that enhance our delivery capabilities and solidify our position as industry leaders. Cultivate Engineering Talent: Formally mentor and coach our junior and mid-level engineers, elevating the team's collective skill set through rigorous code reviews, technical guidance, and career development. What You’ll Bring Experience: You have 7+ years of professional experience in machine learning and software engineering, with at least 2 years in a formal or informal leadership capacity (e.g., tech lead, project lead, or senior mentor). Cloud & Systems Architecture Expertise : You possess a proven ability to architect and deploy scalable, production-grade ML solutions on a major cloud platform (GCP is a significant asset). This includes hands-on experience with Infrastructure-as-Code tools (e.g., Terraform) and designing for distributed computing. Expert-Level Engineering Craftsmanship: You have deep, hands-on expertise in Python for backend ML systems and a mastery of software engineering best practices (e.g., clean architecture, robust testing, CI/CD). You can fluently design and build REST APIs (e.g., using Flask/FastAPI) and are proficient in SQL for complex data manipulation. Consultative Communication & Mentorship : You have an exceptional ability to communicate complex technical concepts to diverse audiences, from C-level stakeholders to junior engineers. You excel at leading technical discussions, presenting solutions, and mentoring teammates to elevate their skills. Bonus Points If You Have: Prior experience in a client-facing or consulting role. Professional Google Cloud certifications (e.g., Professional Machine Learning Engineer). Deep experience with the broader MLOps ecosystem (e.g., Kubeflow, Vertex AI Pipelines, MLflow). Experience building interactive demos for ML models (e.g., using Streamlit, Gradio). What’s in It for You? We believe in empowering our team to thrive, with benefits including: 20 days of paid vacation per calendar year Public Holidays for your Province of Residence 5 Wellness days (sickness, personal time, mental health) 5 Lifestyle days (religious events, volunteer day, sick day) Matching Group Retirement Savings Plan after 3 months Competitive Group Insurance plan on Day 1 - individual premium paid 100%! Virtual Medicine and Family Assistance Program - 100% employer-paid! Home office budget - We are 100% remote! CAD $70/month for internet/phone expenses Company-supplied MacBook Pro or Air CAD $400/year for books, relevant app subscriptions or an e-reader. Opportunities for paid certifications Opportunities for professional and personal learning through Udemy Business Regular company off-sites and meetups Salary: CAD 175,000 - 200,000. Why Datatonic? Datatonic is a UK-based company with an Americas division located in Canada. The Canadian team operates remotely, with members distributed across North and South America. This role is open to candidates located anywhere in Canada. Join us to work alongside AI enthusiasts and data experts who are shaping tomorrow. At Datatonic, innovation isn’t just encouraged - it’s embedded in everything we do. If you’re ready to inspire change and deliver value at the forefront of data and AI, we’d love to hear from you! Are you ready to make an impact? Apply now and take your career to the next level.

Join the team bringing advanced autonomy to the built world At Bedrock, we’re moving AI out of the lab and into the real world. Our team is composed of industry veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we’re deploying autonomous systems on heavy construction machinery across the country, accelerating project schedules of billion-dollar infrastructure projects and improving safety on job sites. Backed by $350M in funding, we’re working quickly to close the gap between America's surging demand for housing, data centers, manufacturing hubs, and the construction industry's growing labor shortage. This is where algorithms meet steel-toed boots. You’ll collaborate with construction veterans and world-class engineers to solve physical-world problems that simulations can’t touch. If you're ready to apply cutting-edge technology to solve meaningful problems alongside a talented team—we'd love to have you join us. Machine Learning Engineer:  Evaluation Bedrock is bringing autonomy to the construction industry! We’re a group of veterans from the autonomous vehicle industry who are passionate about bringing the benefits of automation to areas in the construction industry currently underserved by the market. We’re looking for a highly motivated engineer with experience evaluating complex ML systems deployed in the real world. Your Mission: Translate the infinite nuance of the built world into actionable, AI-native evaluations that accelerate Bedrock Operator adoption. The ideal candidate has hands-on experience in building evaluation systems and designing and executing statistical tests to gauge performance deltas between system iterations. More importantly, you’ve iterated on complex ML systems run in production environments, and you understand the complexities that come with it. What you’ll do: Design and maintain eval systems: Build pipelines for measuring system performance – across open loop and closed loop simulation, hardware in the loop systems, and field data from Bedrock Operator equipped machinery. Excite other teams to gain insights earlier in the development cycle through streamlined workflows. Develop metrics: Connect product goals and system behavior - by bridging real-world specification to measurable indicators from logged data. Empower confident decision making from parameter tuning to program planning by slicing through the noise and delivering objective insights. Classify data sources for training and testing: Implement infrastructure and classifiers - to self-annotate data and allow creation of datasets for a variety of training and evaluation use cases. Leverage models to source rich annotations for massive datasets to accelerate model iteration. Predict system performance: Model metrics and interpret results - from various sources ranging from raw sensor data to key leading indicators. Determine whether new construction sites pose hidden challenges and drive business decisions about deployment readiness. What we’re looking for: Engineers who are currently Senior or Staff level with 5+ years of professional software engineering, data science, or research experience 2+ years of professional experience analyzing modern ML or robotics system performance on real-world problems Proficiency in Python and a data warehouse query language and comfort with development on infrastructure within parallelized cloud-based frameworks Strong statistical analysis skills (e.g. classification, model fit bias determination, hypothesis testing, and uncertainty quantification) Experience working with large datasets ***Bonus points: We’re especially interested in engineers who have applied statistical backgrounds to ML research or real-world robotics applications. Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you. Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.

Join the team bringing advanced autonomy to the built world At Bedrock, we’re moving AI out of the lab and into the real world. Our team is composed of industry veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we’re deploying autonomous systems on heavy construction machinery across the country, accelerating project schedules of billion-dollar infrastructure projects and improving safety on job sites. Backed by $350M in funding, we’re working quickly to close the gap between America's surging demand for housing, data centers, manufacturing hubs, and the construction industry's growing labor shortage. This is where algorithms meet steel-toed boots. You’ll collaborate with construction veterans and world-class engineers to solve physical-world problems that simulations can’t touch. If you're ready to apply cutting-edge technology to solve meaningful problems alongside a talented team—we'd love to have you join us. Machine Learning Engineer: Perception Bedrock is bringing autonomy to the construction industry! We’re a group of veterans from the autonomous vehicle industry who are passionate about bringing the benefits of automation to areas in the construction industry currently underserved by the market. We are looking for engineers with expertise in shipping production 3D perception systems at scale. Successful candidates have architected systems, trained models from scratch, understand the full stack (clustering, detection, classification, and tracking), and have shipped at scale. We use both computer vision and LIDAR-based approaches, so knowledge of either or both is key. Models are just part of the system: you understand data and have good intuition about why models fail. You know how to evaluate corner cases, manage or build data pipelines, use autolabels (or not), and have a strong understanding of statistical properties of these systems. What You’ll Do: Design Early Fusion Architectures: Develop and train state-of-the-art models (e.g., BEV-based transformers) that fuse raw Lidar and Camera data to solve for object detection and semantic segmentation. Tackle "Messy" Physics: Build perception systems robust enough to handle dynamic occlusion (seeing the robot’s own arm/bucket), particulates (dust, snow, rain), and high-vibration conditions. Deploy to the Edge: Optimize models for inference on embedded hardware. You will debug system-level issues, such as sensor calibration drift and latency bottlenecks. Collaborating with other teams to create state-of-the-art representations for downstream use cases. What we're looking for: Production ML Experience: 3+ years of experience taking deep learning models from research to real-world production using PyTorch, Tensorflow, or JAX. 3D Geometry & Calibration: You have a deep understanding of SE(3) transformations, homogeneous coordinates, and intrinsic/extrinsic sensor calibration. You understand the math required to project a 3D Lidar point onto a 2D image pixel accurately. Early Fusion Expertise: Practical experience with architectures that fuse modalities at the feature level (e.g., BEVFusion, TransFuser, PointPainting) rather than just fusing final bounding boxes. SOTA Object Detection experience with modern transformer-based architectures (DETR, PETR, etc…) including similar temporal models (PETRv2, StreamPETR, …) Systems Fluency: You are an expert in Python, but you are also comfortable reading and writing systems code in C++ or Rust. You understand memory management and real-time constraints. Data Intuition: You understand that in robotics, better data alignment often beats a bigger model. You are willing to dig into the data infrastructure to ensure ground truth quality. Ways to stand out: Bonus: Voxel/Occupancy Experience: Experience working with occupancy grids, NeRFs, or voxel-based representations for terrain mapping. Bonus: Top-Tier Research: Published work in conferences such as ICRA, IROS, CVPR, ECCV, ICCV, CoRL, or RSS Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.

About Casap Casap is a Series A startup that has raised over $30M from Emergence, Lightspeed, and Primary Ventures. Based in San Francisco, the company was founded by product leaders from Robinhood and Chime. We are on a mission to change the way banks operate by automating disputes and fighting friendly fraud. People love what we’ve built, from everyday users to the biggest names in finance. Casap is looking for a talented Machine Learning Engineer in San Francisco excited to supercharge how banks operate with a rapidly scaling product at the forefront of automation and Artificial Intelligence. Responsibilities Develop and implement machine learning models to evaluate disputes and chargebacks and likelihood of fraud Create and manage an orchestration layer for multiple models, enabling automated and supervised decision-making processes Collaborate with stakeholders to leverage valuable data from partners and customers, ensuring a continuously learning experience and first-class user experience Qualifications 3+ years of experience designing and deploying ML models in a high-scale production environment. Strong knowledge of machine learning algorithms and data analysis techniques. Enthusiastic about building ML infrastructure from scratch in a nascent environment. Ability to think like a product engineer, balancing technical innovation with practical application and user needs. Strong proficiency in programming languages like Python, R, or similar, and experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn. Excellent problem-solving skills and the ability to work independently and in a collaborative team environment.

About Reducto Reducto is the agentic document platform for leading AI teams who demand enterprise performance at scale. We provide a comprehensive toolkit for working with documents the way a human would, combining custom in-house and leading frontier models to power efficient and accurate document workflows. We’ve grown rapidly, increasing revenue 8x year over year and partnering with hundreds of companies, from leading AI teams like Harvey, Vanta, and Scale, to enterprise customers across FAANG and top trading firms. Reducto has raised over $100M from world-class investors including a16z, Benchmark, and First Round Capital. We would love to meet you if you: Philosophy: You are your own worst critic. You have a high bar for quality and don’t rest until the job is done right—no settling for 90%. We want someone who ships fast, with high agency, and who doesn't just voice problems but actively jumps in to fix them. Experience: You have 2+ years of experience with training, fine tuning, and evaluating ML models used in production systems Language/Skills: You’re exceptional at Python or similar, and are well versed with both traditional computer vision and VLMs Tools: Build your own tools as needed—like a quick Streamlit app to test hypotheses or create a dataset. Approach: A quantitative approach to building products. Ability to debug, experiment, and iterate fast. You should be comfortable getting hands-on with the full development lifecycle, from ideation to shipping to users. The core work will include: Training and deploying new state of the art models for parsing and interpreting unstructured data Experimenting with novel techniques to improve LLM accuracy Build data pipelines, evaluate model performance, and integrate models into the product Working directly with the founders and customers to shape the product direction and engineering strategy Bonus points if you: Have prior experience founding a company or building products at early stages Are ambitious and driven, and care a lot about doing great work with great people Keep up with the latest developments in ML/AI This is an in person role at our office in SF. We’re an early stage company which means that the role requires working hard and moving quickly. Please only apply if that excites you. About Reducto Nearly 80% of enterprise data is in unstructured formats like PDFs PDFs are the status quo for enterprise knowledge in nearly every industry. Insurance claims, financial statements, invoices, and health records are all stored in a structure that’s simply impractical for use in digital workflows. This isn’t an inconvenience—it’s a critical bottleneck that leads to dozens of wasted hours every week . Traditional approaches fail at reliably extracting information in complex PDFs OCR and even more sophisticated ML approaches work for simple text documents but are unreliable for anything more complex. Text from different columns are jumbled together, figures are ignored, and tables are a nightmare to get right. Overcoming this usually requires a large engineering effort dedicated to building specialized pipelines for every document type you work with. Reducto breaks document layouts into subsections and then contextually parses each depending on the type of content. This is made possible by a combination of vision models, LLMs, and a suite of heuristics we built over time. Put simply, we can help you: Accurately extract text and tables even with nonstandard layouts Automatically convert graphs to tabular data and summarize images in documents Extract important fields from complex forms with simple, natural language instructions Build powerful retrieval pipelines using Reducto’s document metadata Intelligently chunk information using the document’s layout data Benefits at Reducto At Reducto, we’re invested in the well-being and growth of our team. Here’s what we currently offer: Unlimited PTO: We believe great work requires recharging. Lunch: Receive a free lunch to eat with your teammates daily at the office Reimbursed Transportation: Provide us with your receipts and we’ll take care of the costs Insurance : Generous health insurance covering medical, dental, and vision. Health and Wellness Budget: We provide up to $150/mo reimbursement for health and wellness spending, such as gym memberships, fitness classes, or similar. Parental Leave: Work with us to build a leave schedule that works for you and your family Reducto is an Equal Opportunity Employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by applicable national, federal, state or local law.

About Reducto Reducto is the agentic document platform for leading AI teams who demand enterprise performance at scale. We provide a comprehensive toolkit for working with documents the way a human would, combining custom in-house and leading frontier models to power efficient and accurate document workflows. We’ve grown rapidly, increasing revenue 8x year over year and partnering with hundreds of companies, from leading AI teams like Harvey, Vanta, and Scale, to enterprise customers across FAANG and top trading firms. Reducto has raised over $100M from world-class investors including a16z, Benchmark, and First Round Capital. The Opportunity As an ML Infra Engineer , you’ll play a key role in building the inference and training frameworks that make it possible to deliver results at scale. You’ll collaborate closely with our ML and Platform teams to scale training across nodes, develop faster and more efficient serving, and create observability across the stack. This is a high-impact role where you’ll help define what high performance ML training and inference look like at Reducto. What You’ll Do Build, and maintain our training and inference stack with an emphasis for fast iteration on training + flexibility for exploring new methods and high performance in inference. Develop benchmarks for both sets of stacks to identify bottlenecks. Explore SOTA advances in training and inference and work to apply them. Design systems for scaling model training across multi-node, multi-GPU environments with strong reliability and observability. Scale distributed training and inference workloads across large GPU clusters while improving utilization, reliability, and cost efficiency. Build the tooling, abstractions, and observability that help ML engineers move faster from experiment to production. You’ll Thrive Here If You: Hold yourself to a high bar for quality and precision. Enjoy solving complex problems and building from first principles. Have strong Python skills + a background in systems engineering. Are comfortable with Kubernetes and distributed training frameworks. Love getting your hands dirty with real-world implementation challenges. Operate well in fast-changing, high-growth environments. Collaborate effectively across technical and non-technical teams. Take full ownership from strategy through execution. Have 3+ years of experience. Bonus points if you: Have experience at an early-stage or high-growth startup. Have developed in open source training/inference stacks in a meaningful way. Are excited to set up distributed inference across 100s-1000s of GPUs. Care deeply about combining technical excellence with business impact. This is an in person role at our office in SF. We’re an early stage company which means that the role requires working hard and moving quickly. Please only apply if that excites you. More about Reducto Nearly 80% of enterprise data is in unstructured formats like PDFs PDFs are the status quo for enterprise knowledge in nearly every industry. Insurance claims, financial statements, invoices, and health records are all stored in a structure that’s simply impractical for use in digital workflows. This isn’t an inconvenience—it’s a critical bottleneck that leads to dozens of wasted hours every week . Traditional approaches fail at reliably extracting information in complex PDFs OCR and even more sophisticated ML approaches work for simple text documents but are unreliable for anything more complex. Text from different columns are jumbled together, figures are ignored, and tables are a nightmare to get right. Overcoming this usually requires a large engineering effort dedicated to building specialized pipelines for every document type you work with. Reducto breaks document layouts into subsections and then contextually parses each depending on the type of content. This is made possible by a combination of vision models, LLMs, and a suite of heuristics we built over time. Put simply, we can help you: Accurately extract text and tables even with nonstandard layouts Automatically convert graphs to tabular data and summarize images in documents Extract important fields from complex forms with simple, natural language instructions Build powerful retrieval pipelines using Reducto’s document metadata Intelligently chunk information using the document’s layout data Benefits at Reducto At Reducto, we’re invested in the well-being and growth of our team. Here’s what we currently offer: Unlimited PTO: We believe great work requires recharging. Lunch: Receive a free lunch to eat with your teammates daily at the office Reimbursed Transportation: Provide us with your receipts and we’ll take care of the costs Insurance : Generous health insurance covering medical, dental, and vision. Health and Wellness Budget: We provide up to $150/mo reimbursement for health and wellness spending, such as gym memberships, fitness classes, or similar. Parental Leave: Work with us to build a leave schedule that works for you and your family Reducto is an Equal Opportunity Employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, physical and mental disability, genetic information, marital status, sexual orientation, gender identity/assignment, citizenship, pregnancy or maternity, protected veteran status, or any other status prohibited by applicable national, federal, state or local law.

General Robotics is an AI research and deployment company building a platform for general robot intelligence. Our mission is to enable rapid, robust, and safe deployment of general intelligence for autonomous systems and robotics. We aspire to become the starting point for AI-powered autonomous systems across a diverse set of scenarios. Position Overview We are seeking an ML Engineer to join our team in Redmond, WA. We build and optimize the platform that serves ML models to robots in real-time — from perception and planning to foundation models — with a focus on low latency, high throughput, reliable and robust robot-to-cloud communication. We are looking for strong candidates who have a background in ML infrastructure and model serving, with experience in areas like CUDA kernel programming; distributed serving frameworks; real-time streaming; and taking research models to production. By applying to this role, you will be considered for multiple teams, such as platform infrastructure, ML systems, and edge deployment. Responsibilities Integrate and productionize state-of-the-art ML models into our serving infrastructure, collaborating with research teams to bring new architectures from prototype to deployment.Contribute to infrastructure tooling that makes onboarding new models faster and more reliable. Develop and maintain low-latency, high-throughput pipelines for ML model inference across robotics workloads. Optimize GPU workloads and accelerate ML frameworks for real-time performance: data transfer, memory management, batching, serialization, and concurrent request handling. Qualifications Bachelor’s degree in Computer Science, Computer Engineering, or relevant technical field, or equivalent practical experience. 1+ years of experience in ML infrastructure, model serving, or backend systems engineering. Strong Python. Comfortable navigating unfamiliar research codebases and turning them into clean, production services. Familiarity with ML frameworks (PyTorch, JAX), containerized deployments (Docker, Kubernetes), and distributed serving frameworks (Ray, Triton, or similar). Familiarity with async Python, real-time communication protocols, and robotics systems is a plus. Familiarity with cloud platforms (AWS, GCP, Azure) and infrastructure-as-code tooling. Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment. Base Salary Range The anticipated base salary for this position is $155000-$200000. Your actual pay will be based on factors such as skills, experience, and location. In addition to base salary, this role is eligible for benefits including medical, 401K, and other health benefits. Work Authorization This role is open to candidates currently based in and authorized to work in the US. Equal Opportunity Employer General Robotics is an equal opportunity employer. We do not discriminate on the basis of any status protected by applicable law. Accommodations If you need a reasonable accommodation during the application or interview process, please contact: hr@generalrobotics.company

Why Join Doppel Doppel is building the future of social engineering defense. Our AI-native platform uses agentic AI to protect executives, employees, customers, and brands from phishing, impersonation, fraud, and other AI-powered threats across digital channels. We help some of the world’s most recognized brands detect and dismantle attacker infrastructure while strengthening employee resilience through threat-informed training and simulation. By unifying Digital Risk Protection, Human Risk Management and Email Security, Doppel connects threats into a real-time intelligence graph to power faster disruption, smarter defense, and modern security awareness at scale. Backed by leading investors including Andreessen Horowitz and Bessemer Venture Partners, and trusted by leading enterprises, Doppel is a rapidly growing Series C startup building the future of social engineering defense. Our team combines deep cybersecurity expertise, operational rigor, and startup velocity to solve some of the internet’s most urgent trust and safety challenges. What We're Building We're building the AI-native social engineering defense platform. This means we're designing scalable systems that monitor billions of domains, social media accounts, apps, dark web forums, etc., and leverage AI agents to identify and neutralize digital threats. What We're Looking For We’re looking for a machine learning engineer to help build and scale the models and systems that power Doppel’s detection systems. Check out our blog post on how we set up our ML platform. As an MLE at Doppel, you will Design, train, and deploy models for both batch and real-time inference that identify malicious or infringing content across diverse data sources. Partner closely with the Detection and Infrastructure teams to ensure our ML systems scale with the volume of web data we ingest Work on problems that range from NLP and embeddings to similarity search, classification, and anomaly detection Collaborate directly with customers and internal stakeholders to translate real-world threats into production ML systems You may be a fit if you: Have experience building and deploying ML systems in production environments Are comfortable working with large-scale datasets and distributed data processing frameworks Understand the trade-offs between research-quality models and production-ready systems Are excited about solving real-world problems where the adversary is constantly evolving What We Offer 🚀 A mission-driven culture with low ego, high ownership, deep customer obsession, and exceptional talent density 🍽️ Free lunch and dinner in the office 🌴 Flexible PTO ✈️ Quarterly team offsites Join Doppel Doppel is the first platform built to dismantle digital deception at scale. We scan over 150 billion entities daily and deploy continuously adaptive AI SOC agents, paired with expert human analysts, to uncover and disrupt the infrastructure behind phishing, impersonation, and online fraud before attacks can spread. Our Threat Grid turns every customer signal into shared intelligence, making each disruption smarter, faster, and more effective. We’re not just another cybersecurity company. We’re defining the future of social engineering defense, where trust is protected, and deception becomes unprofitable. Backed by top-tier investors and trusted by some of the world’s most recognized brands, Doppel is growing fast. If you’re driven to solve real-world problems with bold technology, we’d love to meet you.

Special Notice: This position is NOT contingent upon awarding of a project or needing a funding source. This is full-time employment with webAI. About the Role: We are seeking a Senior Machine Learning Engineer to support our Public Sector initiatives focused on building and optimizing production ready AI systems for secure and distributed environments. You will be responsible for transforming prototype models into scalable, efficient, and reliable production systems that operate seamlessly across a spectrum of hardware from government cloud infrastructure to edge devices in restricted or disconnected environments. Responsibilities: Design, develop, and deploy agentic workflows to orchestrate multi-step reasoning, tool use, and decision-making across production systems. Productionize AI models from research prototypes into scalable, deployable systems used in real world applications. Engineer adaptive ML systems using LoRA, PEFT, and on-device inference strategies, leveraging PyTorch, TensorFlow, and Hugging Face Transformers for model development, fine-tuning, and optimization. Implement model optimization techniques such as quantization, pruning, distillation, and hardware specific acceleration. Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval. Work with multi-modal AI systems across computer vision, audio, and natural language domains. Optimize model execution for distributed and resource constrained environments, ensuring reliability under variable connectivity conditions. Qualifications: Active US Security clearance 4+ years of experience in applied AI, ML engineering, or production AI systems. Deep proficiency in PyTorch, TensorFlow, or Hugging Face Transformers. Proven experience deploying AI models across cloud, edge, and mobile hardware environments. Expertise in model compression and optimization (quantization, pruning, distillation). Experience building RAG pipelines and integrating vector databases (e.g., Quadrant, ChromaDB, FAISS, Milvus, Pinecone). Familiarity with multi-modal models and synthetic data generation methods. Strong algorithmic and problem solving skills, especially in distributed or constrained compute environments. Preferred Skills: Experience with edge AI, federated learning, or offline inference systems. Understanding of AI governance and compliance frameworks relevant to public sector deployments. Experience integrating models into large scale distributed systems or microservice architectures. Excellent communication and technical documentation skills for collaboration across multi disciplinary teams. Strong understanding of GPU computing, CUDA, and performance profiling. We at webAI are committed to living out the core values we have put in place as the foundation on which we operate as a team. We seek individuals who exemplify the following: Truth - Emphasizing transparency and honesty in every interaction and decision. Ownership - Taking full responsibility for one’s actions and decisions, demonstrating commitment to the success of our clients. Tenacity - Persisting in the face of challenges and setbacks, continually striving for excellence and improvement. Humility - Maintaining a respectful and learning-oriented mindset, acknowledging the strengths and contributions of others. Benefits: Competitive salary Comprehensive health, dental, and vision benefits package 401(k) match (U.S.-based employees only) $200/month Health & Wellness stipend Continuing Education support $500/year Function Health subscription (U.S.-based employees only) Free parking for in-office employees Flexible Time Off (FTO) Parental leave for eligible employees Supplemental life insurance webAI is an Equal Opportunity Employer and does not discriminate against any employee or applicant on the basis of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We adhere to these principles in all aspects of employment, including recruitment, hiring, training, compensation, promotion, benefits, social and recreational programs, and discipline. In addition, it is the policy of webAI to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws, regulations and ordinances where a particular employee works.

Role Overview: As a Machine Learning Engineer, you will play a central role in translating cutting-edge machine learning research into scalable, production-ready solutions. You will collaborate closely with cross-functional teams to identify opportunities where ML can drive product value, architect robust model-centric systems, and ensure their seamless integration into real-world applications. The role requires a strong balance between theoretical understanding and engineering execution, with a focus on building reliable, maintainable, and high-impact AI-driven features that align with Nace.AI ’s strategic objectives. Key Responsibilities: Design, build, and maintain end-to-end ML systems, including synthetic data pipelines, model training, debugging, and performance evaluation. Fine-tune large language models (LLMs) and implement meta-learning methods to enhance model generalization and efficiency. Improve existing Nace.AI models by incorporating advancements from recent ML research. Qualifications: Hands-on experience training and fine-tuning large language models (LLMs) and vision-language models (VLMs), including practical work with pre-training, instruction tuning, and alignment techniques (GRPO,RLHF/DPO/PPO). Hands-on Experience with Deep Learning Models, especially Transformers . Ability to translate cutting-edge research from papers into clean, production-ready code ( Paper to Code ). Proven experience scaling inference infrastructure for LLMs/VLMs, including expertise in model serving frameworks like vLLM , TGI. Proficient in Python with a strong track record of building substantial projects. Solid foundation in computer science fundamentals (data structures, algorithms, design patterns). BS degree in CS or related technical field. Solid Experience with ML frameworks and libraries (PyTorch, TensorFlow). Self-starter comfortable working in a fast-paced, dynamic environment. Preferred Qualifications: MS/PhD in CS or related technical field. Familiarity with data processing stacks such as Spark and Airflow. Experience with multi-node GPU training. Contributor to open-source ML projects. Deep knowledge in Linear Programming . Experience with advanced NLP and Multimodal post-training experience (e.g., model distillation, quantization, deployment optimization). Experienced in inference time optimization, deep understanding of LLM serving optimizations for LLMs/VLMs. Hands on experience with quantization techniques (AWQ, GPTQ, FP8/GGUF).

About the company Every physical good spends time in a warehouse, and every warehouse tracks their inventory. Today, nearly 100% of warehouses track their inventory manually using barcode scanners and climbing forklifts. We're Corvus Robotics . Our fully autonomous Corvus One ™ drones use computer vision & robotics to automatically track inventory, improving worker safety and increasing labor efficiency. We believe that data-driven, safe inventory management will optimize the global physical economy and improve economic prosperity for humanity. About the role We are hiring Computer Vision / Machine Learning Software Engineers to build compute-constrained models for deployed robots. You'll tackle diverse technical challenges, working with vast amounts of sensor data to increase environmental awareness and provide customers with deeper inventory insights. We value problem-solving, innovation, and continuous learning, and we encourage exploring new technologies to advance our machine learning capabilities. What you'll do Develop solutions for advanced computer vision tasks, including: Monocular and stereo depth estimation Learning-based structure-from-motion 3D occupancy networks Scene understanding Object detection Optimize performance, accuracy, and speed of compute-constrained models Collaborate across teams to deploy CV/ML models into production Improve ML pipelines and infrastructure for dataset management, training, and deployment Participate in R&D initiatives (20% of time) to experiment with state-of-the-art techniques Drive business value by leveraging your work across robotics, software, and deployment teams This role is in-person hybrid in Mountain View, CA. US work authorization is preferred but not required, can sponsor visas. Must have 5+ years of industry experience in Computer Vision / Machine Learning, Python/PyTorch Expertise in 2D and 3D computer vision techniques Proficiency with Linux, Git, AWS/GCP, and CI/CD workflows Experience in performance engineering for deep neural networks (both training and inference) Knowledge of model optimization techniques for embedded systems, including knowledge distillation, model quantization, and network pruning Adaptive and desire to assume responsibility in a fast-paced startup environment Nice to have Experience with C/C++ Familiarity with ROS (Robot Operating System) Knowledge of model deployment frameworks (e.g., TensorRT, RKNN, OpenVINO, ONNX, CoreML) Experience with TypeScript/JavaScript and Django Experience with data labelers, tooling, and data management

About Cellular Intelligence Cellular Intelligence (CI) is an AI-native TechBio company building a universal foundation model of cell signaling to understand, predict, and ultimately control cellular behavior, transforming biology from trial-and-error into an engineering discipline. We take a full-stack approach, generating perturbation data at over 1,000x the efficiency of conventional methods, capturing millions of time-resolved treatment combinations per experiment, and training large-scale models that generalize across contexts to enable rational protocol design for regenerative medicine, context-specific drug effect prediction, and systematic disease modeling. Our founding team includes repeat AI entrepreneur Dr. Micha Breakstone ( Chorus.ai , acquired for $575M), the Head of the Fundamental AI Group at MIT, and four professors from Harvard Medical School and the University of Washington, including the Chair of Genetics at Harvard, collectively holding five National Academy memberships. Based in Boston, CI has raised over $60M to date from Khosla Ventures, CZI, SciFi VC, and AMD Ventures, and is rapidly scaling its world-class team. Cellular Intelligence's Core Values: We show up – fully accountable, all-in, doing whatever it takes We act with urgency – swift, decisive, proactive We support one another – collaborative, helpful, empathetic Location: Boston, MA (onsite, full-time) About the Role: As a Senior Machine Learning Researcher - Biological Foundational Models, you will play a key role in developing foundational models for single-cell RNA sequencing data. Working closely with other machine learning researchers and computational biologists, you’ll design cutting-edge AI solutions, contribute to pioneering research, and help build the core infrastructure driving Cellular Intelligence’s cell-replacement therapy platform. This role is ideal for a machine learning expert driven by scientific inquiry and eager to pioneer biological discoveries through foundational AI models. Responsibilities: Design, train, and optimize foundational models for single-cell RNA sequencing and other high-dimensional omics data Collaborate closely with computational biologists to ensure models produce biologically meaningful, interpretable outputs, engaging deeply with biological questions and research Design and implement novel deep learning architectures, including transformer-based models tailored to biological data Build scalable, distributed pipelines for training and inference across trillion-token biological datasets Provide mentorship and technical guidance to other machine learning researchers on the team Qualifications: 6+ years of experience in machine learning, transformer-based deep learning, and large-scale data analysis, preferably in biological applications Master’s degree or higher in Computer Science, Artificial Intelligence, Computational Biology, or a related field Strong proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow Proven experience building and optimizing large-scale models, including transformer-based architectures Demonstrated track record of independent research and end-to-end model development, from prototyping to production Passion for AI-driven biology and its potential to transform healthcare Preferred Qualifications: Strongly preferred: PhD with a strong publication record in top machine learning and/or computational biology journals Strongly preferred: extensive background in biological research and biological modeling, ideally with single-cell RNA sequencing data Experience with reinforcement learning (e.g., RLHF, PPO), diffusion models, or multi-modal architectures applied to structured, high-dimensional data Experience in startups or fast-paced environments, with a self-directed, proactive work style Benefits: Take a technical leadership role with a mission-driven company with the potential to significantly impact the lives of millions Work alongside a talented and passionate team at the forefront of AI and cellular biology Contribute to the development of groundbreaking therapies that address significant unmet medical needs Enjoy a competitive salary and benefits package, including flexible work options Exceptional candidates who demonstrate outstanding capabilities and potential will be considered, even if they do not meet every qualification listed. Join us and help unlock the full potential of AI for the benefit of human health! Cellular Intelligence is committed to equal employment opportunity and non-discrimination for all employees and applicants regardless of race, color, ancestry, sex, sexual orientation, age, religion, national origin, ancestry, genetic information, disability, veteran status, marital status, citizenship status, or any characteristic protected under applicable law.

About Finch We believe every American household deserves access to counsel in life’s biggest moments. At Finch, we’re building the infrastructure to make justice radically more accessible. Our modern approach to consumer law automates the admin work and puts clients first, starting with personal injury. In just over a year, we've grown 10x, raised a $20M Series A, and become the pre-litigation partner of choice for top personal injury firms across the country. We believe the best outcomes happen when expert operators and purpose-built AI work together – which is why we handle every step of pre-lit, from intake and claim opening to medical records, lien management, and demands, with humans leading every case. We’re backed by Sequoia, Redpoint, and the founders & CEOs of generational companies like DoorDash, Ironclad, and Digits. We’re rebuilding how the law serves everyday Americans from first principles, and we’re hiring exceptional operators to help us scale it nationwide. This Role Legal work is buried in unstructured documents, repetitive workflows, and data that no existing system handles well — and we're building the AI to fix it. As a Machine Learning Engineer at Finch, you'll own the full lifecycle of AI systems, from prototype to production, working on problems where a single breakthrough can meaningfully change how law firms operate. What You'll Do This is a hands-on applied AI role where you'll build and ship production systems — not just run experiments. The scope will grow as our product and team do. Build voice agents, browser agents, OCR pipelines, and LLM-powered workflows that work reliably in production. Design rigorous evaluation frameworks and feedback loops to systematically improve model accuracy and reliability. Own the full ML lifecycle — model selection, fine-tuning, prompt design, deployment, and monitoring. Collaborate directly with product, ops, and legal experts to make sure the AI is solving the right problems. Track emerging research and tools, and make deliberate calls about when to bring them into our stack. Who We Have in Mind This role is for engineers who care about outcomes over algorithms and are just as comfortable in production as they are in a notebook. Here's what that requires. Must-Haves 3+ years building and deploying production ML systems. Strong Python skills and experience working across the ML stack end-to-end. Hands-on experience with LLMs, prompt engineering, and evaluation design. A track record of shipping observable, maintainable AI systems — not just prototypes. Nice-to-Haves Experience with NLP, OCR, speech, or agent frameworks (LangChain, OpenAI APIs, etc.). Prior work at an early-stage startup where you helped define ML infrastructure from scratch. Familiarity with legal tech, document-heavy workflows, or regulated industries. This Role Might Not Be For You If You prefer research or experimentation over owning systems in production. You do your best work with a well-scoped problem and a stable, established ML platform. You're looking for a remote-first role — this one is 4 days/week in our NYC office. Compensation The expected package for this role includes base salary + commission + equity. Base salary = $180,000 - $280,000. Benefits 100% coverage for health, dental, and vision. 401(k) retirement plan. In-office snacks, drinks, and daily team lunches and dinners. Flexible PTO (we trust you to take the time you need). Interview Process Intro: An initial call to see if there's a mutual fit. Interview: An interview that typically involves a case assignment of sorts. Onsite: A visit to our NYC office to interview, meet the team, and have lunch. At Finch Legal, we believe in practicing what we advocate. As a company dedicated to upholding justice and protecting people in the workplace, we are equally committed to fostering a safe, inclusive, and equitable environment within our own walls. We welcome and support individuals from all backgrounds and lived experiences — regardless of race, ethnicity, gender identity, sexual orientation, religion, disability, or veteran status. We recognize that diversity strengthens our team, enriches our perspectives, and empowers us to better serve our clients and communities. At Finch Legal, inclusion isn’t just a value — it’s a practice.

Raspberry AI Raspberry AI is a leading provider of industry-defining AI design software for fashion brands and retailers. Our software empowers brands to rapidly understand consumer demand and create unique designs within minutes. Leveraging cutting-edge AI analytics and generative AI capabilities, we help fashion brands revolutionize their design and merchandising processes. We are a Series A startup, backed by top-tier venture capital firms such as Andreessen Horowitz, Khosla Ventures, MVP and Greycroft. About the Role This is a full-time remote role for a Senior Machine Learning Engineer at Raspberry AI. We are seeking a highly talented and motivated Machine Learning Engineer to join our growing ML team. In this role, you will focus on improving the quality and performance of our cutting-edge diffusion models, pushing the boundaries of generative AI in the fashion domain. Additional responsibilities may be assigned as business needs evolve. Responsibilities Conduct applied research and experimentation on state-of-the-art diffusion model architectures and training techniques. Implement and evaluate novel techniques for improving quality and controllability in generated designs. Analyze and interpret experimental results, draw meaningful conclusions, and communicate findings effectively. Collaborate closely with the team to translate prototypes into production-ready systems. Stay abreast of the latest advancements in diffusion models, deep learning, and generative AI research. Requirements Master's or Ph.D. in Computer Science, Machine Learning, or a related field. 3+ years of industry experience. Strong theoretical and practical understanding of deep learning, with a focus on generative models (e.g., GANs, VAEs, Diffusion Models). Hands-on experience with deep learning frameworks such as PyTorch. Experience with training and evaluating generative models on cloud GPU platforms (e.g., AWS, GCP, Azure). Proficiency in using and tuning multimodal LLMs, including experience with both API-based and open-source model implementations. Ability to effectively present complex technical information to both technical and non-technical audiences.

Senior / Staff / Principal Machine Learning Engineer Location: Onsite San Francisco (5 days onsite AND hybrid options) We have multiple startups interested in talent. Here is a generic summary. Instead of a perfect job description, we present talented individuals to companies and allow them to share how that talent fits in the organization. Key Responsibilities: Model Development: Designing and implementing ML algorithms and models, including deep learning models. Data Handling: Preprocessing, analyzing, and preparing large datasets for model training and evaluation. System Integration: Collaborating with software engineers to integrate ML models into production systems. Performance Optimization: Continuously improving and optimizing ML models for accuracy, efficiency, and scalability. Monitoring and Maintenance: Monitoring model performance in production, troubleshooting issues, and ensuring model reliability. Staying Updated: Keeping abreast of the latest advancements in ML, AI, and related technologies. Collaboration: Working with data scientists, software engineers, and other stakeholders to deliver effective ML solutions. Essential Skills: Programming Languages: Strong proficiency in Python, R, or other relevant languages. ML Frameworks: Experience with frameworks like TensorFlow, PyTorch, or scikit-learn. Data Science Fundamentals: Solid understanding of statistical analysis, data modeling, and machine learning algorithms. Problem-Solving: Excellent analytical and problem-solving skills to address complex challenges. Communication: Effective communication skills to convey technical information to both technical and non-technical audiences. Collaboration: Ability to work effectively in a team environment. Education and Experience: A bachelor's or master's degree in computer science, engineering, mathematics, statistics, or a related field is typically required. Several years of experience in machine learning, data science, or software development is often preferred. Compensation: Market range and can include equity – details can be provided after the specific client is determined.

Location: Onsite — Austin, TX Employment Type: Direct Hire, Full‑Time Job Title: Senior ML Engineer Company Overview 9 Mothers Defense develops AI-enabled systems to counter unmanned aerial threats. Our first product, EDDA, is an autonomous counter-sUAS point-defense platform designed to detect, track, and neutralize Group 1 drone threats. The company is headquartered in Austin, Texas. Position Summary 9 Mothers is seeking a Machine Learning Engineer to design, train, and maintain the models that power our counter-sUAS perception stack. The Machine Learning Engineer is responsible for model research, dataset engineering, and the training infrastructure that supports detection, classification, and discrimination of aerial targets. This is an individual contributor position. We don’t use RAG, LLMs, or pre-built cloud APIs. Our stack requires building from the ground up to solve high-stakes problems under strict Size, Weight, and Power (SWaP) constraints. You should be capable of building models from scratch and have a fundamental understanding of the problem space. Essential Duties Design, train, and iterate on machine learning models for detection, classification, and tracking of aerial targets. Own the dataset pipeline end-to-end, including data collection, labeling, curation, augmentation, synthetic data generation, and closed-loop retraining based on field performance. Build and maintain training infrastructure, including experiment tracking, compute orchestration, and evaluation harnesses. Define metrics and evaluation methodologies that correlate to real-world operational performance. Support deployment of trained models into the production perception stack, and address discrepancies between training and deployed performance. Analyze field data to identify and address model failure modes. Requirements Demonstrated experience shipping machine learning systems into production under real-world operational requirements. Fluency in PyTorch or JAX, including full training loop development beyond fine-tuning off-the-shelf models. Strong software engineering skills beyond model development, including ownership of training infrastructure. Proficiency in Python; working knowledge of C++ or Rust at the training-to-deployment boundary. U.S. citizenship and ability to pass a background check. Preferred Qualifications Experience with detection or tracking of small, fast, or adversarially perturbed targets. Synthetic data generation and sim-to-real methodologies. Experience training models for edge deployment, including quantization-aware training and knowledge distillation. Prior experience in defense or other safety-critical machine learning applications. Active security clearance, or eligibility to obtain one. Passion for building robots or engineering projects as a hobby Benefits Meaningful Early Equity: You aren't just an employee; you are a foundational owner. Your contributions directly drive the value of your stake in the company. Direct Roadmap Influence: Forget the bureaucracy of big defense. You will have a seat at the table, directly shaping our product and technology trajectory from day one. Mission-Critical Work: We don't build for "what if." We build systems the Department of War actively needs to counter immediate, real-world threats. The Builder's Playground: Work in a brand-new lab fully optimized for rapid prototyping, equipped with NVIDIA Jetsons, high-end scopes, and 3-D printers. 100% Employer-Paid Premiums: We cover 100% of your medical, dental, and vision insurance premiums and cover 50% of healthcare premiums for your dependents. Unlimited PTO: We value results, not clock-watching. Take the time you need to stay sharp and recharge. Zero Red Tape: You report to the founders. You have the autonomy to make technical decisions that would take months of committee approval at a larger firm. Austin-Based Culture: Join an onsite team in Austin, TX, where we prioritize high-bandwidth collaboration and rapid field-testing. Relocation Assistance: We want the best talent in the room. If you aren't in Austin yet, we’ll help you get here, to make your transition to the Silicon Hills seamless. About the Interview 1. Application screen phone call (30 min) 2. Live coding test with the Director of Engineering (45 min via MS Teams) 3. Paid take-home consulting agreement ($500, ~4-8 hours) 6. Onsite interview in Austin 7. Offer Equal Employment Opportunity 9 Mothers is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. Export Control This position may require access to information or technology subject to U.S. export control laws and regulations, including the International Traffic in Arms Regulations (ITAR). U.S. citizenship is required.

About Suno We're building the world's first creative entertainment platform, where the entire world can feel the joy and fulfilment of making music. Music is for everyone: Our users include everyone from grandmothers creating songs for their loved ones, to Grammy winners using Suno Studio, our power tool, to make the most popular hits in the world. Building the future of entertainment requires ambition. The pace is fast, the problems are hard, and the work demands ownership and intensity. For the right people, it’s incredibly rewarding: a chance to shape a new medium, work with a small team that cares deeply about quality, make music, drink too much coffee, and build something that millions of people use to express themselves in ways that were never before possible. Suno is the fastest growing consumer entertainment company and the leader in AI music. We are backed by leading investors including Bond Capital, Menlo Ventures, Lightspeed Venture Partners, IVP, Forerunner, Union Square Ventures, Alkeon, Quiet, Matrix Partners, Schroders Capital and, NVentures (venture arm of NVIDIA). About the Role We’re looking for early members of our research team. You’ll work closely with the founding team and have ownership of a wide variety of technical decisions on how we build and deploy our state of the art ML models trained with an H100/scientist ratio of >100x. Check out our Suno version of the job here: https://suno.com/s/YaZAme1qvoqFJOi1 What You’ll Need 5+ years experience training state of the art models with distributed pytorch Intimate familiarity of the entire stack of data engineering, designing, training and evaluating machine learning models Track record showing independent ownership of entire research projects from start to finish Extensive experience training large generative models from scratch (LLMs or diffusion models) A love of music (listening, exploring, making) is a huge plus Bachelor's degree or equivalent required. Additional Notes: Applicants must be eligible to work in the US. Compensation The annual salary range for this role is $160K – $280K + target equity + benefits (including medical, dental, vision, and 401k) Perks & Benefits for Full-Time Employees Company Equity Package 401(k) with 3% Employer Match & Roth 401(k) Medical, Dental, & Vision Insurance (PPO w/ HSA & FSA options) 11 Paid Holidays + Unlimited PTO & Sick Time 16 Weeks of Paid Parental Leave Creative Education Stipend Generous Commuter Allowance In-Office Lunch (5 days per week) Suno is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the Massachusetts Fair Chance in Employment Act, NYC Fair Chance Act, LA City Fair Chance Ordinance, and San Francisco Fair Chance Ordinance.

At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses. The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity. Who We Are We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully. This role develops and scales large-scale machine learning training systems for multimodal robotics data, enabling the creation of high-performance autonomy models. By optimizing distributed training pipelines, neural network architectures, and data processing workflows, the position improves training efficiency, accelerates model iteration, and maximizes GPU utilization. The role collaborates closely with ML researchers and infrastructure teams, influencing the design, deployment, and performance of end-to-end autonomy models and the large-scale data pipelines that support them. Responsibilities Design and maintain training systems that can process and learn from petabyte-scale multimodal datasets (e.g., video and point cloud data). This includes ensuring data is efficiently loaded, distributed, and processed across large GPU clusters. Identify and resolve bottlenecks in the training pipeline, including data loading, preprocessing, model computation, and inter-node communication, to maximize GPU utilization and reduce training time. Work with the ML team to develop and refine neural network architectures suitable for autonomy tasks, particularly those handling high-dimensional and sequential sensor data. Create and adjust loss functions and training strategies that help the model learn effectively from complex multimodal inputs and improve autonomy performance. Configure, monitor, and maintain large-scale distributed training jobs across multiple machines and GPUs, ensuring stability, fault tolerance, and efficient resource usage. Implement scalable systems to preprocess, transform, and augment large robotics datasets so that they are suitable for model training. Work closely with ML scientists and other engineers to integrate new models, experiments, and training approaches into the production training pipeline. Analyze training metrics, model outputs, and experiment logs to assess model performance and guide improvements in architecture, data usage, or training strategies. Develop tools and workflows that allow teams to run experiments, track results, and iterate quickly on new model ideas or training approaches. Qualifications Master’s or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a closely related technical discipline. Minimum of 5 years of professional experience developing, training, and deploying machine learning models in production environments. Hands-on experience training machine learning models across multiple GPUs or compute nodes, including familiarity with distributed training frameworks and large dataset handling. Strong programming skills in Python for implementing machine learning models, data pipelines, and training workflows. Solid knowledge of core concepts such as neural networks, optimization algorithms, loss functions, model evaluation, and training methodologies. What Makes You Stand out Experience identifying and resolving training bottlenecks related to compute utilization, memory usage, and data throughput in machine learning systems. Experience training machine learning models on robotics or autonomous driving datasets involving multimodal sensor inputs such as camera video, LiDAR point clouds, radar, or telemetry data. Experience developing models that combine multiple data modalities (e.g., images, point clouds, and structured sensor data) into a unified learning system. Peer-reviewed publications or significant research contributions in machine learning, robotics, or related areas. * Please note: The listed base salary range applies to candidates based in the US. Compensation may vary depending on location, experience, and role alignment. We are open to qualified candidates working remotely in Canada Canada - ALL: $177k - $215k CAD

Output has built a biological reasoning model that understands biology at the scale and complexity life actually operates. Our model independently learned the principles of molecular interactions, opening up drug treatments that were previously impossible. We're already generating therapies that traditional approaches cannot reach. The hardest problems in both AI and biology are being solved here, and there is room for you to own one. Output is currently in stealth, operated by a team of repeat founders and biotech veterans with multiple exits in AI x Bio, and backed by top-tier VCs including Y Combinator. Our internships offer flexible commitment, with a minimum of 20 hours per week, ranging 12 to 24 weeks. We have various start dates available to accommodate your academic schedule. There may be opportunities for full-time employment upon successful completion of your PhD. The Role You will own a research project that directly advances Output's research and its path to new therapies. This is not a side project: your work will contribute to the same models and methods the full-time team builds on. We will select a project together based on your research interests and our priorities, with a path to publishing your work at top-tier venues and the opportunity to continue with additional projects throughout the year. About You You are currently pursuing a PhD in machine learning, computer science, computational biology, physics, mathematics, or a related field You have a strong research track record, demonstrated by publications or submitted work at venues such as NeurIPS, ICML, ICLR, or relevant computational biology conferences You have hands-on experience designing and running ML experiments, including training models and analyzing results You are proficient in Python and PyTorch, and comfortable working with large-scale datasets and GPU infrastructure You can work independently on a research problem: scoping an approach, running experiments, interpreting results, and communicating findings clearly Bonus Points You have experience applying machine learning to biological, chemical, or molecular data You have a background in computational biology, biophysics, chemistry, or a related natural science You have experience with generative models, representation learning, or self-supervised learning You have contributed to open-source machine learning or computational biology projects Our Values ❤️ Heart: We foster a culture of ownership. We are assembling a team of individuals who are passionate and take pride in their contributions. 🏆 Excellence: We have an unwavering commitment to excellence and continuously challenge ourselves to reach the highest standards. 🚀 Practicality: We value practicality and results-oriented thinking. We are committed to making a tangible impact on the lives of patients and the broader community. 📣 Honesty: We place a high value on honesty and directness. We firmly believe in addressing issues as they arise, in an open and transparent manner. 🎮 Fun: We believe that life is too short to not have fun. Our goal is to create a workplace that is fun, engaging, rewarding and fulfilling. What We Offer We encourage new and different ideas, creativity and contrarian thinking Healthy feedback focused environment to help you strive - leadership will have high expectations, regularly share constructive feedback, support you and help you grow, and welcome receiving feedback and ideas from you You own your day-to-day management. What we care about is that we all hit our milestones Competitive salary and equity in a growing, well-funded startup Excellent medical, dental, and vision coverage

You will be responsible for developing and implementing machine learning solutions that power our AI-native platform. This role requires expertise in custom model development, transformer architectures, and production deployment of ML systems. Responsibilities Design and develop custom machine learning models for specific business use cases. Implement and optimize transformer architectures for natural language processing tasks. Develop and maintain ML pipelines for data preprocessing, model training, and inference. Work with large language models and implement fine-tuning strategies. Implement retrieval-augmented generation (RAG) systems and optimize their performance. Collaborate with engineering teams to deploy ML models in production environments. Monitor and maintain model performance in production, implementing retraining strategies as needed. Conduct research on emerging ML techniques and evaluate their applicability to our platform. Mentor junior ML engineers and contribute to best practices within the team. Required Skills and Qualifications Master's degree or PhD in Computer Science, Machine Learning, or a related field, or equivalent practical experience. 5+ years of experience in machine learning development and deployment. Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow, or similar). Deep understanding of transformer architectures and their applications in NLP. Experience with large language models and fine-tuning techniques. Proficiency in implementing and optimizing RAG systems. Experience with ML model deployment and production monitoring. Strong understanding of data preprocessing, feature engineering, and model evaluation techniques. Experience with cloud platforms and ML infrastructure (AWS SageMaker, Google Vertex AI, or similar). Excellent problem-solving skills and attention to detail. Strong communication and interpersonal skills. Preferred Qualifications Experience with MLOps and ML pipeline orchestration tools. Knowledge of distributed training and model optimization techniques. Experience with vector databases and similarity search algorithms. Familiarity with reinforcement learning and multi-agent systems.

Our mission is general causal intelligence, AI that is capable of (1) predicting the future and (2) identifying the optimal actions to change that future. To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because domains governed by physics have inherent cause and effect relationships, unlike visual or textual data. Weather is the ideal training ground for an LPM. It is the most well-observed physical system, offering rapid, objective ground truth feedback from sensory observations and data at a scale that dwarfs what is used to train today’s LLMs. Causal Labs is a team of researchers and engineers from self-driving, drug discovery, and robotics - including Google DeepMind, Cruise, Waymo, Insitro, and Nabla Bio - who believe general causal intelligence will be the most important technical breakthrough for civilization. We look for infrastructure engineers who are excited to tackle unsolved problems. Our training and inference challenges demand deep expertise in setting up distributed training clusters and optimizing performance for large models. If you have experience building large-scale ML infrastructure in related fields such as language and vision models, robotics, biology -- join us on this mission. Responsibilities Design, deploy, and maintain large distributed ML training and inference clusters Develop efficient, scalable end-to-end pipelines to manage petabyte-scale datasets and model training throughout the entire ML lifecycle Research and test various training approaches including parallelization techniques and numerical precision trade-offs across different model scales Analyze, profile and debug low-level GPU operations to optimize performance Stay up-to-date on research to bring new ideas to work What we’re looking for We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains. Strong grasp of state-of-the-art techniques for optimizing training and inference workloads Demonstrated proficiency with distributed training frameworks (e.g. FSDP, DeepSpeed) to train large foundation models Knowledge of cloud platforms (GCP, AWS, or Azure) and their ML/AI service offerings Familiarity with containerization and orchestration frameworks (e.g., Kubernetes, Docker) Background working on distributed task management systems and scalable model serving & deployment architectures Understanding of monitoring, logging, observability, and version control best practices for ML systems You don’t have to meet every single requirement above.

Orchard Robotics is a Series A startup backed by top VCs like Quiet Capital, Shine Capital, and General Catalyst. We're securing America’s food supply by building the AI farmer that automates our nation’s farms. We've raised over $25M in pursuit of our mission to help farmers farm more profitably and sustainably than ever before. What We Do: We start by collecting the most valuable data for farmers, telling them everything about what is growing on their millions of trees, across thousands of acres of farmland. We do this using advanced camera systems we build, that take pictures of every one of the billions of fruit in a farm. This data lives in our cloud data platform, FruitScope, that we've developed from the ground up to help farmers manage their crops with precision. Farmers across the nation use our industry-leading software to look at their data, make critical decisions, and command farming operations on a daily basis. Our technology is used today across some of the largest farms in the nation. The Role: In order to analyze billions of fruit on farms all year long, our advanced, tractor-mounted camera systems have to know a.) precisely where they are, and b.) everything about the fruit they are seeing. We are looking for a Senior Machine Learning Engineer to build creative, practical, and robust solutions to ML/CV software and infrastructure problems, relating to training edge ML models on massive amounts of real-world farm image data collected by our camera systems. About the role: As an early engineer, you'll receive generous equity compensation Full-time role at our San Francisco, CA office Flexible working hours Comprehensive Health, Vision, and Dental coverage, and we cover 100% of the premium We move fast, and sometimes this means staying late or working weekends Our team is close-knit & highly driven, you’ll work directly with our CEO and entire team We’re deeply motivated by the impact we’re making – every line of code written or new system built means less food that goes to waste, and more people who are fed. What you’ll do: Build and maintain scalable ETL pipelines for processing large, diverse image datasets collected from our tractor-mounted camera systems in farms. Stay up-to-date with current literature in computer vision models and architectures, and apply relevant advancements to our systems. Develop, deploy, and monitor infrastructure for model training, evaluation, and inference, both in the cloud and on edge devices. Design and implement intelligent active sampling infrastructure to optimize data collection and improve model performance. Collaborate with a multidisciplinary team to integrate ML solutions into production robotics systems. Work closely with agronomists and farmers to understand crop biology and translate domain knowledge into actionable ML features. Be a generalist, supporting different parts of our software stack as needed. What makes you a good fit: 5+ years of experience building production-grade data pipelines and ML infrastructure. Proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch). Strong experience with data engineering tools (e.g., Pandas, SQL, Apache Airflow, Spark). Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Experience working with massive amounts of real-world training data. Familiarity with MLops software and data engineering to ensure consistent deployment of ML models. Ability to work independently, learn quickly, and operate in a dynamic environment Enthusiasm for taking on multiple roles and responsibilities as our company grows. Bonus Points: Experience deploying & optimizing ML models to run fast on embedded compute like NVIDIA Jetson Experience prototyping, evaluating, or deploying new ML/CV models on the edge. If you're looking to help make a positive impact in the world by building the future of farming, come join us!

Orchard Robotics is a Series A startup backed by top VCs like Quiet Capital, Shine Capital, and General Catalyst. We're securing America’s food supply by building the AI farmer that automates our nation’s farms. We've raised over $25M in pursuit of our mission to help farmers farm more profitably and sustainably than ever before. What We Do: We start by building AI-powered camera systems that collect the most valuable data for farmers, telling them everything about what is growing on their millions of trees, vines, and plants, across thousands of acres of farmland. Our state-of-the-art AI analyzes every one of the billions of fruit across a farm. We provide accurate yield estimates, fruit counts, size projections, disease detection, inventories, bloom maps, and more! All this data lives in our cloud platform, FruitScope OS, that we've developed from the ground up to enable farmers to manage their crop with precision. Today, our technology is trusted by some of the largest farms in the nation. We are growing fast, and have the industry-leading product. Farmers use our software every day to make critical decisions and run more efficient, profitable operations. The Role: In order to analyze billions of fruit on farms all year long, our advanced, tractor-mounted camera systems have to know a.) precisely where they are, and b.) everything about the fruit they are seeing. We are looking for a Machine Learning Engineer to build creative, practical, and robust solutions to ML/CV software and infrastructure problems , relating to training edge ML models on massive amounts of real-world farm image data collected by our camera systems. About the role: Full-time, in-person role at our San Francisco or Seattle office. As an early engineer, you'll receive generous equity compensation Comprehensive Health, Vision, and Dental coverage, and we cover 100% of the premium We move fast, and sometimes this means staying late or working weekends Our team is close-knit & highly driven, you’ll work directly with our CEO and entire team We’re deeply motivated by the impact we’re making – every line of code written or new system built means less food that goes to waste, and more people who are fed. What you’ll do: Build and maintain scalable ETL pipelines for processing large, diverse image datasets collected from our tractor-mounted camera systems in farms. Develop and deploy infrastructure for model training, evaluation, and inference, both in the cloud and on edge devices. Design and implement intelligent active sampling infrastructure to optimize data collection and improve model performance. Stay up-to-date with current literature in computer vision models and architectures, and apply relevant advancements to our systems. Collaborate with a multidisciplinary team to integrate ML solutions into production robotics systems. Work closely with agronomists and farmers to understand crop biology and translate domain knowledge into actionable ML features. Be a generalist, supporting different parts of our software stack as needed. What makes you a good fit: 2+ years of real-world, industry experience building production-grade data pipelines and ML infrastructure. Proficiency in Python and experience with ML frameworks (e.g., PyTorch). Strong experience with data engineering tools (e.g., Pandas, SQL, MLFlow, WandB). Familiarity with cloud platforms (AWS, GCP) and containerization (Docker, Kubernetes). Experience working with massive amounts of real-world training data. Familiarity with MLops software and data engineering to ensure consistent deployment of ML models. Ability to work independently, learn quickly, and operate in a dynamic environment Enthusiasm for taking on multiple roles and responsibilities as our company grows. Bonus Points: Experience deploying & optimizing ML models to run fast on embedded compute like NVIDIA Jetson Experience prototyping, evaluating, or deploying new ML/CV models on the edge. If you're looking to help make a positive impact in the world by building the future of farming, come join us!

Join the Sleep Fitness Movement At Eight Sleep, we’re on a mission to fuel human potential through optimal sleep. As the world’s first sleep fitness company, we’re redefining what it means to be well-rested and building the most advanced hardware, software, and AI technology to make it possible. Our products power peak mental, physical, and emotional performance by transforming every night of sleep into a personalized, data-driven recovery experience. We are trusted by high performers, professional athletes, and health-conscious consumers in over 30 countries worldwide. Recognized as one of Fast Company's Most Innovative Companies in 2019, 2022, and 2023, and twice named to TIME's “Best Inventions of the Year.” We operate like a high-performance team: fast, focused, and motivated by impact. We don’t just ship; we iterate, refine, and obsess over the details that help our members sleep better and wake up stronger. Every role at Eight Sleep is a chance to create cutting-edge technology, collaborate with world-class talent, and help shape a future where sleep isn’t passive - it’s a powerful tool for living better. If you’re tired of the ordinary and driven to build at the edge of what’s possible, this is your moment. Join us and lead the movement that’s transforming how the world sleeps and what we’re all capable of when we wake up. High Standards. No Apologies We operate with intensity because our mission demands it. At Eight Sleep, we bring the same mindset as the world’s top performers: focused, relentless, and always pushing to be in the top 1% of our craft. Think Kobe Bryant’s mamba mentality, applied to bold ideas, next-gen tech, and flawless execution. This isn’t a 9-to-5. We’re a team that puts in the extra effort, not because it’s required, but because we care about the impact of our work. We’re here to build fast, push limits, and deliver without compromise. If you thrive under pressure and want to do the most meaningful work of your career, you’ll feel right at home. If you’re looking for something easier – this isn’t it. The role We’re looking for a Machine Learning Engineer to build and ship consumer-facing AI systems that power personalization, coaching, and next-generation “sleep intelligence.” You’ll work across data, modeling, product, and engineering to translate research into reliable, measurable improvements for members. This role is ideal for someone who loves end-to-end ownership: from problem framing → prototyping → offline evaluation → online experimentation → production deployment → iteration. How you’ll contribute Build and deploy ML models that improve sleep experiences through personalization, prediction, and behavior understanding (e.g., readiness forecasting, event detection, individualized recommendations). Apply and adapt foundation-model capabilities to real product workflows (LLM + tools/RAG, multimodal modeling, policy learning), including MCP-style integrations where helpful. Develop user behavior models that connect longitudinal signals (sleep, environment, routines) to actionable interventions - grounded in robust experimentation and measurement. Design evaluation strategies (offline metrics, slice-based analysis, calibration, reliability, fairness) and partner with Product to run high-quality online experiments. Productionize models: scalable training/inference pipelines, model monitoring, drift detection, alerting, and continuous improvement loops. Collaborate with cross-functional partners (Product, Mobile, Backend, Clinical) to scope requirements and ship high-impact features. What you need to succeed Minimum Qualifications 2+ years building ML systems in production, ideally for consumer-facing products. Strong ML fundamentals across supervised learning, sequence/time-series modeling, and modern deep learning. Hands-on experience with large-scale model training and evaluation (PyTorch/TensorFlow/JAX), and strong Python engineering practices. Experience with personalization systems (ranking/recommendations, segmentation, lifecycle modeling, propensity/behavior modeling, causal/experiment-aware thinking). Fluency with data tooling (SQL, distributed compute such as Spark/Ray, and cloud storage/compute). Strong product sense: you can translate ambiguous goals into measurable outcomes and iterate quickly with stakeholders. Bonus Points Experience applying LLMs/foundation models to product features (tool use, retrieval, structured outputs, guardrails, evals). Experience with multimodal data (sensor signals + context) and/or health/biometrics data. Experience with privacy-preserving approaches (on-device/federated learning, differential privacy, data minimization). Experience designing experimentation frameworks or causal inference approaches for personalization. Why join Eight Sleep? Innovation in a culture of excellence Join us in a workplace where innovation isn’t just encouraged - it’s a standard. Our flagship product, the Pod, is a testament to our culture of excellence, beloved by hundreds of thousands of customers worldwide. At Eight Sleep, you will be part of a team that continuously pushes the boundaries of technology in sleep fitness. Immediate responsibility and accelerated career growth From your first day, you’ll take on substantial responsibilities that have a direct impact on our core business and product success. We are a small team that empowers you to own your projects and see the tangible effects of your efforts, enhancing both your professional growth and our company’s trajectory. Your path will be challenging but rewarding, perfect for those who thrive in fast-paced environments aiming for high standards. Collaboration with exceptional talent Work alongside other bright minds like you: at Eight Sleep exceptional intelligence and a passion for breakthroughs are the norms. Our team members are not only experts in their fields but also avid innovators who thrive in our dynamic, fast-paced environment. Equitable compensation and continuous equity investment We extend equity participation to every full-time team member, recognizing and rewarding your direct contributions to our success. This includes periodic equity refreshments based on performance, ensuring that as Eight Sleep grows and succeeds, so do you – perfectly aligning your achievements with the broader triumphs of the company. Pay grows rapidly as you accumulate experience with Eight Sleep and translate it into concrete impact. Your own Pod - and other great benefits Every Eight Sleep employee receives the very product that defines our mission: a Pod of their own. If you join us you’ll get your own Pod, along with other benefits. Learn more at eightsleep.com/careers At Eight Sleep we continually celebrate the diverse community different individuals cultivate. As an equal opportunity employer, we stay true to our values by ensuring everyone feels they can flourish and grow. We are committed to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

About the Company Rain makes the next generation of payments possible across the globe. We’re a lean and mighty team of passionate builders and veteran founders. Our infrastructure makes stablecoins usable in the real-world by powering card transactions, cross-border payments, B2B purchases, remittances, and more. We partner with fintechs, neobanks, and institutions to help them launch solutions that are global, inclusive, and efficient. You will have the opportunity to deliver massive impact at a hypergrowth company that is funded by some of the top investors in fintech, crypto, and SaaS, including Sapphire Ventures, Norwest, Galaxy Ventures, Lightspeed, Khosla, and several more. If you’re curious, bold, and excited to help shape a borderless financial future, we’d love to talk. Our Ethos We believe in an open and flat structure. You will be able to grow into the role that most aligns with your goals. Our team members at all levels have the freedom to explore ideas and impact the roadmap and vision of our company. About the Team The fraud risk management team at Rain creates sophisticated, scalable risk mitigation solutions to protect our customers and deliver a low-friction experience. We achieve this by maintaining transaction and lifecycle event monitoring, building alerts to speed fraud detection and response, and creating risk rules and strategies powered by ML models. We are a pillar of the business, supporting new products and ensuring their success. Rain’s next-generation payment technology introduces new fraud vectors that require holistic, end-to-end thinking, strong data fundamentals, and fraud management savvy to combat. What you’ll do Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis Develop and maintain end-to-end ML pipelines: data ingestion, feature engineering, model training, deployment, and continuous monitoring Design and implement low-latency, real-time decision systems partnering with fraud risk data scientists, integrating with transaction or behavioral data streams Own ML infrastructure, including model versioning, automated retraining, and safe deployment strategies (e.g., shadow, rollback) Build robust monitoring and alerting for model performance, latency, data quality, and drift Lead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases Develop tooling and processes to improve the effectiveness and speed of the ML development lifecycle Partner with platform teams to meet strict SLAs for availability, latency, and accuracy Collaborate closely with talented engineers, data scientist and compliance teams across Rain Work in a fast-paced environment on a rapidly growing product suite Solve complex problems at the intersection of ML systems, data, and reliability What we're looking for 5+ years of experience building ML systems in production; at least 2+ in fraud, risk, or anomaly detection domains A degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field Proven track record designing and maintaining ML models at scale Advanced proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn) Strong understanding of supervised/unsupervised learning, anomaly detection, and statistical modeling Ability to work autonomously, manage ambiguity, and collaborate closely with data scientists to translate analytical models into robust fraud prevention systems Experience developing, validating, and productionalizing predictive real-time and offline fraud detection models using supervised and unsupervised ML techniques Experience collaborating with cross-functional teams to prioritize, scope, and deploy MLI solutions at scale Nice to have, but not mandatory Domain expertise in banking, payments, or transaction monitoring Experience with graph-based or network-level fraud detection techniques A graduate degree in Computer Science, Engineering, Statistics, Applied Math, or a related technical field Experience fine-tuning or adapting generative AI / large language models for pattern generation or synthetic data augmentation (in partnership with data science) Knowledge of model governance, bias mitigation, and regulatory compliance in fraud contexts Things that enable a fulfilling, healthy, and happy experience at Rain: Unlimited time off 🌴 Unlimited vacation can be daunting, so we require Rainmakers to take at least 10 days off. Flexible working ☕ We support a flexible workplace. If you feel comfortable at home, please work from home. If you’d like to work with others in an office, feel free to come in. We want everyone to be able to work in the environment in which they are their most confident and productive selves. New Rainmakers will receive a stipend to create a comfortable home environment. Easy to access benefits 🧠For US Rainmakers, we offer comprehensive health, dental, and vision plans for you and your dependents, as well as a 100% company subsidized life insurance plan. Retirement goals 💡Plan for the future with confidence. We offer a 401(k) with a 4% company match. Equity plan 📦 We offer every Rainmaker an equity option plan so we can all benefit from our success. Rain Cards 🌧️ We want Rainmakers to be knowledgeable about our core products and services. To support this mission, we issue a card for our team to use for testing. Health and Wellness 📚 High performance begins from within. Rainmakers are welcome to use their card for eligible health and wellness spending like gym memberships/fitness classes, massages, acupuncture - whatever recharges you! Team summits ✨ Summits play an important role at Rain! Time spent together helps us get to know each other, strengthen our relationships, and build a common destiny. Expect team and company off-sites both domestically and internationally.

About Plaud Inc. Plaud is building the world's most trusted AI work companion for professionals to elevate productivity and performance through note-taking solutions, loved by over 1,500,000 users worldwide since 2023. With a mission to amplify human intelligence, Plaud is building the next-generation intelligence infrastructure and interfaces to capture, extract, and utilize what you say, hear, see, and think. Plaud Inc. is a Delaware-incorporated, San Francisco-based company pushing the boundary of human–AI intelligence through a hardware–software combination. With SOC 2, HIPAA, GDPR, ISO27001, ISO27701, and EN18031 compliance, Plaud is committed to the highest standards of data security and privacy protection. To learn more about Plaud, please visit https://www.Plaud.ai and follow along on Instagram , X , Facebook , LinkedIn , and YouTube Why You Should Join Us Plaud is building the next generation intelligence infrastructure and interfaces to capture, extract, and utilize intelligence from what people say, hear, see, and think. Plaud is a bootstrapped, skyrocketing, profitable company with a $250M revenue run rate achieved in just three years. Define the next-gen paradigm for human-AI interaction. Gain exposure to cutting-edge AI for Pro tools and play a direct role in our global expansion. Work with passionate teammates who value innovation, collaboration, and customer success. Grow your career in a culture that champions continuous learning and fast career development. Market-competitive compensation, global exposure, and a vibrant, creativity-fueled work atmosphere. You may be a good fit if you: Have a passion for turning ambiguous, subjective concepts like a voice's naturalness, expressiveness, or conversational cadence into clear, defensible, and automated metrics that researchers and leadership can rely on. Possess strong software engineering skills (especially in Python) and have experience building reliable distributed systems, data pipelines, or evaluation harnesses that can run at scale against live model checkpoints. Can deeply partner with ML researchers to define exactly what "good" looks like for a Speech LLM, translating capabilities (like ASR robustness in noisy environments or TTS emotional steerability) into measurable benchmarks. Are comfortable building and owning dashboards that track model health during training, improving signal-to-noise ratios, reducing evaluation latency, and making performance regressions impossible to miss. Rapidly debug anomalous mid-training results to determine if a drop in performance stems from the model architecture, corrupted data, or infrastructure. Communicate complex statistical results and model behaviors clearly to both technical and non-technical stakeholders. Strong candidates may also have experience with: Speech Metrics: Deep familiarity with both traditional (WER, CER, PESQ, etc) and modern audio evaluation frameworks (automated MOS scoring). LLM-as-a-Judge: Using frontier models or finetune multi-modal LLMs to evaluate the conversational logic, transcription accuracy, audio quality, and reasoning of audio models. Human Evaluation: Managing large-scale crowdsourcing operations or preference data collection to support RLHF/DPO efforts. Observability: A strong background in statistics and experimental design, paired with experience building trusted tracking dashboards (e.g., Weights & Biases, MLflow). Adversarial Datasets: Curating complex datasets to test edge cases, such as heavy accents, overlapping speech, or highly noisy acoustic environments. What We Offer Founding Team Initiative: Opportunity to be an early, foundational member of our core SpeechLLM lab, with meaningful ownership and impact on a fast-growing startup. Competitive Compensation: $200K - $365K base salary + performance bonus + Equity. Comprehensive Benefits: Top-tier healthcare for employees and dependents, including dental and vision, and a generous employer subsidy. Retirement Planning: 401(k) plan for full-time employees with company matching. Paid Time Off: Unlimited PTO, plus 13 paid holidays. New Parent Leave: 12 weeks of paid time off to spend time with your new family, regardless of gender. Hybrid Office: Minimum of 3x in-office per week to foster highly collaborative, fast-paced research. Gear & Perks: Choice of top-of-the-line laptops/workstations, annual offsites, and a fully stocked office. Plaud is and will continue to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristics.

About Poshmark Poshmark is the leading fashion marketplace where style comes alive through discovery, self-expression, and human connection. Powered by a vibrant community of 165 million members, Poshmark brings real people and taste to shopping through a social experience shaped by shared discovery. Buying and selling fashion feels simple, joyful, and personal, while every item tells its own story. Poshmark empowers sellers to grow meaningful businesses, keeps fashion in circulation longer, and gives shoppers access to unique and trusted finds, from everyday pieces to one-of-a-kind vintage and luxury. The Machine Learning team is a central player in the Poshmark organization. Our mission is to build a world-class machine learning platform to bring value out of data for us and for our customers. Our goal is to democratize data science and machine learning, support exploding business, and use machine learning to drive value across the chain (Search, personalization, fraud detection, catalog digitization to name a few). Responsibilities: Manage the entire ML lifecycle from data collection to deployment and monitoring Collaborate across teams such as DS, QA, Infra and other engineering teams to productionize ML models Write and optimize code for production environments, ensuring the robustness and reliability of ML services at scale Manage and support current solutions while evolving them to incorporate newer technologies Strong written, verbal, and presentation skills, with the ability to convey complex concepts in a clear and simple manner Stay updated with the latest developments in data science and machine learning Desired Skills & Experience: 4+ years of experience applying Machine Learning to concrete problems at large scale Bachelors or Masters in Computer Science, Statistics, or related field Strong CS fundamentals. Should be able to write algorithms with ease. Solid understanding of Data Science and ML fundamentals – Regression, Classification, Tree-based approach, Neural network, and sequence-based models. Working experience with at least one ML model: LLMs, GNN, Deep Learning, Logistic Regression, Gradient Boosting trees, etc. Should have excellent understanding of ML lifecycle Good understanding of system architecture. Have knowledge of big data technologies – streaming architecture, data pipelines, etc. Experience with Python, SQL, Java or Scala Good to Have: Big data systems – Spark, EMR, S3, AirFlow Production experience with LLMs and prompt engineering Experience with Flask, FastAPI, RabbitMQ, Embeddings and Vector DBs Our Techstack: Pytorch, Tensorflow, Sklearn Flask, Fastapi, Gunicorn, Quarkus MLFlow, Sagemaker, Kibana, Airflow, Databricks, Grafana, Datadog Docker, Kubernetes, Jenkins Redis, Redshift, MongoDB, Spark, Kafka, RabbitMQ, Milvus

Compare.com is a start-up company that has changed the way consumers shop for car insurance in the United States. We are Great Places to Work Certified, are expanding our product breadth and are looking to add the best talents to our team. Are you up for the challenge? If so, keep reading. Who will love this job: This position is a great opportunity for an entrepreneurially - minded person who enjoys making a difference in a start-up environment. We are looking for an experienced Machine Learning Engineer to complement the team to help create and extend existing models. Compare.com views Machine Learning (ML) as an integral component of our strategy, and we want you to help us build ML as a competency for Compare.com from the ground up. The ideal candidate will have strong analytical curiosity and a passion for applying advanced modelling techniques in problem solving. He or she must be self-motivated, dependable, and determined team player and be able to communicate complex concepts in simple terms and can tie analytic results to business outcomes. What you'll be responsible for: Building extending, deploying, maintaining, and troubleshooting ML solutions to help drive business metrics. Developing and initiating projects from start to finish. Collaborating with developers, product owners and other team members to rapidly build, test and deploy robust algorithms and data analytic solutions. Collaborating with Analytics and Data teams to optimize ML model performance and data pipelines. Ensuring our relevancy in the field of ML by staying up to date on the latest ML techniques and become the subject matter expert within the company. Being an advocate for data science, working with senior managers throughout the business, supporting helping them understand where and when it can add value to their processes. Developing solutions to complex problems without considerable technical direction. Creation of meaningful datasets for modelling and evaluation from a vast quantity of data in multiple formats. Communicating results to key decision-makers Other duties as assigned - we're a start-up! You are an ideal candidate for this role if you: Technical: Degree in quantitative discipline (Statistics, Math, Data Science, Computer Science, Engineering) 2+ years of experience as a machine learning engineer or data scientist Experience in the end-end deployment of Machine Learning Models Proven and demonstrated experience with data preparation, building, scaling, and optimizing machine learning solutions 2+ years of experience with developing software in object-oriented programming languages, including Python, Scala, Java, or C++ Experience with machine learning tools, including TensorFlow, Pytorch, or Scikit Learn Experience with DB structured query language (e.g. SQL) High degree of comfort and experience operating within an Agile environment (e.g., Kanban, Scrum) Experience utilizing or building CI/CD pipelines Experience utilizing and implementing components in cloud-based infrastructure (e.g., Azure, Amazon Web Services) Proficiency writing code and understanding object-oriented paradigms Non-Technical: Are self-driven, organized, dependable and a determined team player Have a positive outlook and desire to drive and instill data quality throughout the organization Think analytically and solve complex problems within a team structure Are flexible and able to adapt to new processes and requirements in a dynamic environment Have strong, clear, and concise written and verbal communication skills Can effectively communicates technical information to non-technical people as and when required Are an advocate for innovation and drive change through persuasion and consensus Can translate issues/problems into solutions with tangible changes that improve the team or the organization Are flexible and able to adapt to new processes and requirements in a dynamic environment Have a positive outlook and desire to drive and instill Technical Excellence within the IT dept. Have strong, clear, and concise written and verbal communication skills Can communicate technical information to non-technical people as and when required Can plan and manage your own workload within multiple projects, delivering results within agreed deadlines Can proactively demonstrate new ideas and innovation At this time, Compare.com will not sponsor a new applicant for employment authorization for this position. Compare.com is an equal opportunity employer committed to diversity in the workplace and promotes a drug-free environment. While we are open to remote work, we will not be able to support employees based out of CA, CO, MA, NY or NJ. Our Business Compare.com is a website which is reinventing how consumers shop for services in the US. Our aim is to bring new transparency to the auto insurance market and to help people save money. We intend to be the same for other online comparison shopping, branching out to other related insurance and unrelated financial services. Our People & Culture So, what's it like to be an employee of Compare.com? At Compare.com, every day is different, and we are different, maybe even a bit counterculture. Flexibility is key, as we plan for long-term but adjust continuously. We firmly and wholeheartedly believe that people who like what they do, do it better and we go out of our way to ensure that coming to work is enjoyable. Doing the job and doing it well is the key no matter who you are. At Compare.com we are a team, we work as a team and if we fall, we fall as a team and lean on each other to pick us up. Effective interaction with the team is required, as is challenging each other's ideas to come to the best possible solution. We are looking for someone who comes with strong problem-solving skills and a wealth of good ideas on their own, but also recognizes the inherent creativity in all of us and accepts willingly that the next great idea may not be their own genesis. Compare.com offers an honest and open culture and every member of our staff is treated as an equal. Our benefits and 401k package is highly competitive, as is our work/life balance, flexible work schedules, and generous PTO structure. Achievement is recognized and rewarded, but most of all coming to work is fun. We work hard to produce high quality work, but we like to play as well. These days that involves more Slack trivia, Drawasaurus and Zoom cooking classes together than it does ping pong and happy hours. Benefits & Perks Competitive Compensation, Bonus Program, & Stock Scheme Health, Dental, Vision, and Disability coverages HSA + generous employer contribution annually 401k + company match Generous PTO + Flexible Hours Paid Parental Leave program Wellness Fund Training & Development Stipend

Machine Learning & Big Data Researcher – Network and Security Group Open Positions : multiple Overview We are looking for a highly motivated researcher to partner with our network and security group to conduct research and development in the areas of machine learning & big data. Job Duties · Conducting research and development · Design and implement machine learning, deep learning and big data analytics for different types of data. Examples of data types include text, network, security, time series, social media, and scientific data. · Design, implement and test software and hardware prototypes · Summarize research results, write research reports and proposals Required skills & abilities · Previous experience in one or multiple of the related areas, including but not limited to: distributed computing, big data, internet of things, cloud computing, machine learning, text analytics, networking, embedded systems, mobile devices, and cyber security · Proficient programming skills using Python, Java, or C/C++ · Good writing and communication skills Education requirements · A degree (BS, MS or PhD) and/or a research background in the areas of Computer Science, Computer Engineering and/or Electronic Engineering ABOUT IAI Intelligent Automation, Inc. (IAI) is a technology innovation company headquartered in Rockville, MD. For over 30 years, we have specialized in providing advanced technology solutions and R&D services to federal agencies and corporations throughout the United States and internationally. Leveraging agile R&D processes, a multi-disciplinary collaborative environment, and its substantial intellectual property portfolio, IAI excels in developing concepts into market-focused products and customer-driven solutions. IAI’s core R&D areas include: Air Traffic Management, Big Data and Social Media Analytics, Control and Signal Processing, Cyber Security, Education and Training Technologies, Health Technologies, Modeling and Simulation, Networks and Communications, Robotics, and Sensor Systems. US Citizenship or Permanent Residency required All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.

Summer Intern - Big Data & Machine Learning

About Velocity-X: Velocity-X, a VelocityBlack company, constructs and deploys data management and analytics solutions for the defense and intelligence communities. We're proud to boast a world-class engineering team that thrives on rolling up their sleeves to solve your mission's biggest challenges. Velocity-X is seeking a highly motivated and self-directed professional to fill the role of Machine Learning (ML) Engineer to support our team in Northern Virginia. Responsibilities: Design, develop, and implement machine learning models and algorithms to solve specific business problems. Build and maintain scalable and robust machine learning pipelines for data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. Transform machine learning models into deployable APIs and integrate them with existing applications and infrastructure. Collaborate closely with data scientists, software engineers, and product managers to understand requirements and translate them into practical ML solutions. Experiment with different machine learning techniques and algorithms to identify the most effective approaches for given problems. Evaluate model performance using appropriate metrics and iterate on models to improve accuracy, efficiency, and scalability. Monitor and maintain deployed models, ensuring their reliability and performance in production environments. Troubleshoot and resolve issues related to machine learning models and pipelines. Stay up-to-date with the latest advancements in machine learning, deep learning, and related fields. Contribute to the development of best practices and standards for machine learning development and deployment within the team. Document machine learning models, experiments, and deployment processes. Potentially work with large datasets and big data technologies. Optimize machine learning models for performance and efficiency. Qualifications: Master's in computer science, Machine Learning, or higher level degree is preferred with of 3+ years of related industry experience in Machine Learning, Computer Science, Data Science or related fields. Demonstrated hands-on experience in developing and deploying machine learning models in a production environment. Strong programming skills in Python and experience with relevant machine learning libraries and frameworks such as TensorFlow, Keras, PyTorch, scikit-learn, etc. Solid understanding of machine learning algorithms (e.g., regression, classification, clusting, dimensionality reduction, deep learning architectures). Experience with data preprocessing, feature engineering, and data visualization techniques. Familiarity with data storage and processing technologies (e.g., SQL, NoSQL databases, Spark, Hadoop). Experience with cloud platforms (e.g., AWS, Azure, GCP) and their machine learning services. Understanding of software development principles, version control (e.g., Git), and CI/CD pipelines. Strong analytical and problem-solving skills with the ability to interpret data and draw meaningful conclusions. Excellent communication and collaboration skills to effectively communicate technical concepts to both technical and non-technical audiences. Preferred Skills: Experience with specific areas of machine learning such as Natural Language Processing (NLP), Computer Vision, or Recommender Systems. Experience with MLOps practices and tools for automating and monitoring machine learning workflows. Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes. Experience with building and deploying RESTful APIs. Familiarity with big data technologies and distributed computing. Experience with statistical modeling and inference. Position Clearance Requirement: TS/SCI with Full-Scope Polygraph Equal Opportunity Employer: Velocity-X is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law.

nou Systems, Inc. (nSI) is a 100% ESOP company solving challenging defense problems in an enjoyable, stimulating environment. We support mission areas including missile defense, battle management, cybersecurity, test range modernization, biotechnology, and space control. We are pleased to announce an opening for a Machine Learning Engineer who will join our team in Colorado Springs, Colorado. You will help design, develop, and deploy machine learning capabilities across research, prototype, and operationally relevant defense applications. We are looking for a mid-level engineer with strong fundamentals in machine learning, backend software development, and data science. The ideal candidate can collaborate with teammates to move ML capabilities from concept toward usable systems. Responsibilities Develop, train, evaluate, and analyze machine learning models using Python and PyTorch, writing modular, maintainable, and testable code. Build backend software components that support ML workflows, data processing, model evaluation, and system integration. Help identify, prepare, and validate training and evaluation datasets. Implement and adapt ML methods from open literature, including supervised learning and deep learning approaches. Work in Linux-based, containerized development environments using VS Code Dev Containers, Remote-SSH, Docker/Podman, and contribute to continuous improvement of development processes. Use GitLab-based workflows for source control, issue tracking, merge requests, code review, and collaboration. Communicate technical results clearly through written documentation, presentations, and team discussions. Support multiple project teams and help translate ML concepts into practical engineering solutions. Basic Qualifications Bachelor's degree in computer science, mathematics, software engineering, data science, or a closely related technical field. 3+ years of professional experience in machine learning, data science, or backend software development. Hands-on experience developing, training, or evaluating deep learning models using PyTorch. Professional experience building backend software, data pipelines, APIs, or system integration components. U.S. citizenship and the ability to obtain a Secret security clearance. Preferred Qualifications Strong communication skills, intellectual curiosity, and comfort working across ML, software, and mission-domain teams. Our work often requires creative, multidisciplinary approaches. Understanding of core ML and statistical concepts (bias-variance tradeoff, data mismatch, sample sufficiency) Comfort with self-direction. You'll work with our top technical talent, but we're looking for evidence you can diagnose a problem and approach it strategically. Experience with Docker, Podman, or similar containerization tools, including editing Dockerfiles or container configuration. Experience with ML tools (MLflow, Optuna, or PyTorch Lightning) Experience with RL development using Gymnasium and Ray RLlib. Experience with LLMs, generative AI tools, vector databases, or frameworks such as LangChain or LlamaIndex. Familiarity with CI/CD pipelines, Kubernetes, DevSecOps practices, secure artifact repositories, or deployment in restricted DoD environments. Position Details This is an in-office role requiring presence at least 4 days per week. Annual Salary Range: $100,000 - $160,000 nou (pronounced 'new') Systems: Since its founding in 2012, nou Systems has built a reputation for excellence in innovative engineering, prototype development, and technical and professional services in multiple markets, including missile defense, cybersecurity, test range modernization, and space control. Our diverse and highly skilled team delivers state-of-the-art products from concepts and prototypes to fully developed and integrated solutions. We offer significant advancement and personal career development opportunities within our dynamic high-tech company. Our culture is firmly established in treating our employees like family. Benefits of working at nSI nou Systems, Inc. offers a comprehensive, total rewards package that includes competitive compensation and diverse benefits that reflect our company culture of service, excellence, and a supportive work environment. Benefits may vary based on status but the majority of our positions include the following: Competitive Wages* Medical, Rx, Dental & Vision Insurance Medical plan with Health Savings Account eligibility Generous company-funded Basic Life Insurance Company-funded Short-Term & Long-Term Disability Optional Accident and Critical Illness Insurance Personal Time Off, Annual Leave, and Paid Federal Holidays 401(k) Retirement Plan Employee Stock Ownership Plan (ESOP) Tuition Reimbursement for ongoing training, continuing education, or advanced degree programs Personal Development, Learning Opportunities, & Lunch-n-Learns Opportunities for Advancement Skills Development & Certifications Employee Referral Bonus Program Corporate Sponsored Events & Community Outreach *Final compensation for this position is determined by a variety of factors, such as a candidate's relevant work experience, skills, certifications, and geographic location. nSI is an Equal Opportunity Employer Employment opportunities at nSI are based upon a candidate's qualifications and capabilities to perform the essential functions of a particular job and are free from discrimination based on race, color, religion, national origin, sex, sexual orientation, gender identity, age, disability, protected veteran status, genetic information, or any other characteristic protected by law. For our complete EEO/AA and Pay Transparency statement, please visit www.nou-systems.com/workingatnou . U.S. citizenship is required for most positions.

Position Summary: The IT Division is responsible for the development and operations of information systems for the State and Federal agencies doing business related to or using information from the administration of motor vehicles and driver licenses. The Machine Learning (ML) Data Engineer position has core responsibilities for the design, development, deployment, and operational support of machine learning solutions on cloud infrastructure. This includes the full model lifecycle - from data acquisition and dataset preparation through feature engineering, experimentation, model training, validation, production deployment, and ongoing monitoring. Current applications include anomaly detection across high-volume messaging networks, but the scope encompasses any ML capability that strengthens system reliability, operational intelligence, and data-driven decision-making across AAMVA systems. Essential Duties and Responsibilities: We are seeking a talented Data Engineer with machine learning experience to join our team. You will design, build, and operationalize ML solutions running on cloud infrastructure (Azure or AWS). You will work across the full model lifecycle: preparing datasets, engineering features, running experiments, deploying models to production, and operating them on cloud infrastructure. As a detail-oriented professional, you have a strong track record of independently managing projects and driving them to successful completion. Your statistical foundation and engineering discipline enable you to move from exploratory analysis through to production-grade, monitored solutions. You communicate clearly with both technical and non-technical stakeholders - translating model behavior, data constraints, and engineering trade-offs into terms that drive decisions. You operate effectively across the broader IT organization, with sufficient general IT fluency to understand how ML systems interact with infrastructure, security, operations, and business workflows, and you proactively build those connections rather than working in a data silo. Key responsibilities include: Designing and building dataset preparation pipelines - acquiring, cleaning, transforming, and versioning data for ML training and evaluation Engineering features that extract meaningful signals from structured and semi-structured data sources (time-series patterns, statistical profiles, categorical encodings) Running structured experimentation - testing multiple algorithms against defined scenarios, measuring performance, and documenting findings Training, evaluating, and tuning ML models including regression, classification, clustering, anomaly detection, and ensemble methods Deploying models to production on cloud infrastructure and building the pipelines that keep them running (retraining, scoring, threshold management) Monitoring model performance in production - tracking drift, false positive rates, and detection efficacy over time Building and maintaining batch and streaming data pipelines using Synapse, Fabric, Spark, and Event Hubs that feed ML systems Writing and optimizing analytical queries (SQL, KQL, PySpark) for data exploration, statistical profiling, and real-time analysis Creating validation frameworks - synthetic test data generation, backtesting against historical logs, and shadow-mode evaluation Building dashboards and visualizations that communicate model outputs to technical and non-technical stakeholders Collaborating with cross-functional teams to identify ML opportunities and translate operational problems into data solutions; communicating findings, trade-offs, and model behavior clearly to technical and non-technical audiences across IT, operations, and leadership Direct Reports: None QUALIFICATIONS Formal Education: Bachelor's degree in computer science, data science, statistics, mathematics, or related quantitative field. Equivalent work experience may be substituted Knowledge, Skills, and Abilities: Basic Qualifications 3-5 years of hands-on experience in data engineering, ML engineering, or applied analytics Hands-on cloud platform experience (Azure or AWS) building and deploying data or ML solutions on managed cloud services; specific platform less important than depth of experience Working knowledge of statistical foundations: distributions, variance, standard deviation, trend vs. seasonality, hypothesis testing, and how to apply them to real operational data Experience with the ML experiment-to-production cycle: dataset preparation, feature engineering, model training, evaluation, and deployment Proficiency in Python for data processing, statistical analysis, and ML model development Strong SQL skills with understanding of relational database fundamentals: data modeling, query optimization, indexing strategies, and how SQL Server infrastructure supports production workloads (T-SQL, stored procedures, Availability Groups) Experience building data pipelines that handle batch and streaming workloads Experience with version control systems (Git) and CI/CD practices Strong problem-solving skills, attention to detail, and ability to work independently on ambiguous problems Strong written and verbal communication skills - able to explain technical findings to non-technical stakeholders and engage productively across IT, operations, and leadership; comfort operating outside the ML silo and contributing to broader technology discussions Preferred Qualifications Experience with time-series analysis, anomaly detection, or statistical process control on operational data Familiarity with unsupervised and semi-supervised techniques (isolation forest, clustering, ensemble methods) Experience building and managing ML model lifecycle on Azure (MLflow, Fabric ML, Azure ML) or AWS (SageMaker, Glue, Step Functions) Familiarity with KQL (Kusto Query Language) for time-series decomposition, log analytics, or real-time data exploration Knowledge of data modeling and dimensional modeling concepts Experience with synthetic test data generation and model validation frameworks Familiarity with operations and monitoring of mission-critical data platforms Technical Stack Core Technologies: Microsoft Fabric, Azure Synapse Analytics, Apache Spark, Delta Lake, Azure Event Hubs ML & Analytics: scikit-learn, PySpark ML, statistical modeling, time-series analysis, feature engineering, model validation Languages: Python, SQL, PySpark, KQL, C# Data Infrastructure: T-SQL, Stored Procedures, SQL Server Availability Groups Azure Services: Azure Functions, Azure Data Factory, Azure Key Vault Optional: Databricks, Snowflake, Lakehouse Architecture, Azure OpenAI; AWS candidates: equivalent services (SageMaker, Glue, Kinesis, Redshift) are acceptable in place of Azure-specific stack items Visualization: Power BI Development: Azure DevOps, CI/CD Disclaimer Statement: The preceding job description has been written to reflect management's assignment of essential functions. It does not prescribe or restrict the tasks that may be assigned. AAMVA is an Equal Opportunity Employer/Veterans/Disabled

POSITION OVERVIEW Intelligent Automation, Inc. (IAI) is seeking team members who are outstanding candidates with a background in both AI /machine learning and traffic engineering at all levels (Entry, Mid, and Senior). Candidates will work in a highly agile and dynamic R&D environment that focuses on applications of AI/machine learning to transportation research, traffic management, and operations. The candidates should support (and/or lead) the research, development, and integration of Machine and Deep Learning in the areas of traffic data, video, time series signal analysis. JOB DUTIES • Depending on the experience and interest, job duties will include: • Design and implement AI and deep learning algorithms for traffic detector/video data, time series data, social media, network, and text analytics • Design and develop the architecture and framework for a cloud-based analytics-as-a-service platform to handle high volume traffic data • Design, develop and integrate web services to support existing software applications • Design, enhance, and maintain existing software and perform upgrades to existing live production software applications • Support monitoring of deployed services and applications DESIRED SKILLS • Prior experience with one or more of the following technologies is a plus, but not required. • Eager and motivated to perform research, learn and develop new techniques. • Machine/Deep Learning Libraries: TensorFlow/Keras/PyTorch, OpenCV, Matlab • Hands on experience with Deep Convolutional Neural Network, Deep Reinforcement Learning • Programming languages/platforms: Python, Java/JDK, HTML5, JavaScript, C/C++ • Database experience: MongoDB, MySQL/SQL, InfluxDB, Prometheus, Accumulo • Traffic Simulation Software Tools, such as VISSIM • Operating Environment/Services: Linux, Windows, Android/iOS EDUCATION A bachelor's degree (or higher) in Computer Science, Engineering, Civil or related field is required. ABOUT IAI Intelligent Automation, Inc. (IAI) is a technology innovation company headquartered in Rockville, MD, conducts state-of-the-art research and product development in the areas of Robotics, Signal Processing and Control, Sensor Technology, Distributed Intelligent Systems and other areas (visit www.i-a-i.com). We specialize in providing advanced technology solutions and R&D services to federal agencies, and corporations throughout the United States and internationally. IAI's core R&D areas include Air Traffic Management, Big Data, and Social Media Analytics, Control and Signal Processing, Transportation Research/Development and Services, Cyber Security, Education, and Training Technologies, Health Technologies, Modeling and Simulation, Networks and Communications, Robotics and Sensor Systems. For more information on IAI, please visit www.i-a-i.com. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status. IAI is an EEO/AA employer.