{"has_next":true,"jobs":[{"id":"60c7aa2a-21b2-4ed4-997e-01e06f7425d0","company_id":"a0000000-0000-0000-0000-000000000003","title":"Director, Enterprise Machine Learning \u0026 Research","slug":"director-enterprise-machine-learning-research-1923b033","description":"The Enterprise ML team works on the front lines of the AI revolution, partnering deeply with customers to identify high-impact business problems and build cutting-edge AI systems using Scale’s proprietary research, data, and infrastructure—unlocking domain expertise through high-quality data and expert feedback.\n As Director of Enterprise ML, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.\n This role is ideal for a leader who thrives in ambiguity, understands both frontier GenAI capabilities and their limitations, and is motivated by turning research into durable, production-ready systems.\n What You’ll Do \n \n Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments).  \n Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution.\n Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes.\n Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally.\n Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent.\n Stay deeply connected to the research community, understanding major trends, and helping set them.\n Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.\n \n What We’re Looking For \n Core Qualifications \n \n 8+ years of hands-on research experience (PhD or equivalent preferred) in machine learning, deep learning, generative models, agent/rl systems or related domains.\n A strong track record of research excellence, including publications in top-tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.).\n Experience and track of recording in landing major research impacts in a fast-paced environment\n Experience leading or managing research teams. You’re excited to mentor, coach and develop talent.\n Excellent written and verbal communication skills. You are able to articulate research ideas and outcomes to both technical and non-technical stakeholders.\n Exceptional communication and stakeholder management skills, with the ability to influence executives, customers, and cross-functional partners\n \n Nice to Have \n \n Hands-on experience building and deploying agent-based, tool-augmented, or workflow-driven LLM systems in enterprise environments\n Prior ownership of enterprise AI platforms, internal ML products, or customer-facing AI services at scale\n Proven track record of partnering directly with enterprises to identify high-impact use cases and deliver measurable business outcomes\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:\n $289,800 — $362,250 USD \n 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. \n About Us: \n 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","salary_min":289800,"salary_max":362250,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["deep-learning","llm","generative-ai","machine-learning","research"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4679727005","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-31T18:05:38Z","expires_at":"2026-06-29T14:01:07.494675Z","created_at":"2026-04-13T09:36:42.207592Z","updated_at":"2026-05-30T14:01:07.606238Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/60c7aa2a-21b2-4ed4-997e-01e06f7425d0"},{"id":"3e448289-7fed-4d06-9da9-bd0879a8241b","company_id":"a0000000-0000-0000-0000-000000000003","title":"Manager, Machine Learning Research Scientist, GenAI","slug":"manager-machine-learning-research-scientist-genai-c7602476","description":"Scale AI accelerates the development of AI systems by providing the data, infrastructure, and tooling that power the most advanced models in the world. Our teams operate at the intersection of cutting-edge research, large-scale engineering, and real-world deployment, partnering with leading frontier labs, enterprises, and government agencies to push Generative AI into new capabilities and applications.\n As AI rapidly evolves from static models to dynamic, agentic systems, Scale is building the foundational research, evaluation methodologies, and agent/RL infrastructure that will define this next era. You’ll join a high-impact research organization driving advances in large-language models, post-training, evaluation, and agentic/RL environments, helping shape how next-generation AI is built, measured, and deployed.\n As a Research Scientist Manager, you will lead a world-class team of research scientists and engineers, define the research roadmap, and drive execution from early prototyping to deployment. You’ll thrive in a fast-moving environment, balancing deep technical leadership with people management, vision setting, and delivery.\n You will: \n \n Lead, mentor and grow a team of research scientists and engineers working on GenAI research initiatives (e.g., evaluation, post-training, agents, RL environments).  \n Define and drive a multi-year research roadmap: identify key scientific questions, set milestones, allocate resources, and ensure rigorous execution.\n Collaborate cross-functionally with engineering, product, client-facing teams and external academic or industry partners to translate research into components, insights, and actionable outcomes.\n Communicate compellingly: publish research, present at conferences, engage in open-source contributions, and represent the team externally.\n Drive an inclusive, high-performing culture: help your team through technical challenges, provide growth opportunities, and attract top talent.\n Stay deeply connected to the research community, understanding major trends, and helping set them.\n Thrive in a high-energy, fast-paced startup environment and are ready to dedicate the time and effort needed to drive impactful results.\n \n Ideally you'd have: \n \n 5+ years of hands-on research experience (PhD or equivalent preferred) in machine learning, deep learning, generative models, agent/rl systems or related domains.\n A strong track record of research excellence, including publications in top-tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.).\n Experience and track of recording in landing major research impacts in a fast-paced environment\n Experience leading or managing research teams. You’re excited to mentor, coach and develop talent.\n Excellent written and verbal communication skills. You are able to articulate research ideas and outcomes to both technical and non-technical stakeholders.\n \n \n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:\n $398,400 — $498,000 USD \n 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. \n About Us: \n 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 \u0026 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. \n We believe that everyone ","salary_min":398400,"salary_max":498000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["agents","generative-ai","deep-learning","machine-learning","research"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4631811005","is_featured":true,"is_sticky":false,"status":"active","published_at":"2025-11-19T00:07:25Z","expires_at":"2026-06-29T14:01:10.349946Z","created_at":"2026-04-13T09:36:44.631119Z","updated_at":"2026-05-30T14:01:10.459208Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3e448289-7fed-4d06-9da9-bd0879a8241b"},{"id":"78754b10-1caa-42cb-a933-ccfae8797f70","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Machine Learning Engineering Manager, App SW","slug":"machine-learning-engineering-manager-app-sw-5e031225","description":"About us    \n 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.\n 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.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n   The Role  \n 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.\n 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.\n 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.\n 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.\n  \n Key Responsibilities: \n \n Build and lead a self-sufficient AV development team in the US, hiring and mentoring top talent across Robotics and ML.\n Deliver autonomy capabilities tailored to road conditions and driving norms, in close collaboration with central Autonomy teams.\n Drive full-cycle development: from identifying local autonomy needs, to designing, implementing, testing, and deploying features into production.\n Ensure the team upholds Wayve’s high engineering standards, while operating with agility and independence.\n Work closely with OEM partners in the US — representing Wayve’s autonomy team in technical discussions, capturing product requirements, and shaping joint development plans.\n Establish close working relationships with our product and vehicle operations teams in the US.\n \n About you  \n 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.\n Essential \n \n A strong background in robotics and autonomy, with experience building and deploying systems that operate in real-world environments.\n Demonstrated ability to lead and grow high-performing engineering teams, ideally in geographically distributed or independent settings.\n Comfortable with ambiguity: you can define goals, carve out roadmaps, and deliver high-impact work with minimal supervision.\n Broad technical fluency: capable of reviewing and guiding work across software engineering, ML, controls, and systems integration.\n Excellent communication skills: you’re able to clearly convey technical context and strategic vision across cultures and time zones.\n Strong product sense and stakeholder management skills: you’re comfortable interfacing directly with OEM customers and representing engineering in external-facing conversations. \n \n Desirable \n \n Prior experience in autonomous vehicles or robotic systems operating at scale.\n Familiarity with US's road environment, driving behaviour, or AV regulatory landscape.\n A strong foundation in machine learning and its application to real-time decision-making or perception systems.\n \n  \n 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.\n 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.  \n #LI-KM1 \n Wayve is committed to creatin","salary_min":336400,"salary_max":381600,"location":"Sunnyvale, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["generative-ai","autonomous-vehicles","robotics","machine-learning"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8571171002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T16:04:34Z","expires_at":"2026-06-29T14:12:44.929157Z","created_at":"2026-05-30T14:12:45.041753Z","updated_at":"2026-05-30T14:12:45.041753Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/78754b10-1caa-42cb-a933-ccfae8797f70"},{"id":"e0e00a39-c16a-434f-bfa5-59174ea0c816","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Machine Learning Engineering Manager, App SW","slug":"machine-learning-engineering-manager-app-sw-101e63ec","description":"About us    \n 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.\n 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.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n   The Role  \n 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.\n 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.\n 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.\n 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.\n  \n Key Responsibilities: \n \n Build and lead a self-sufficient AV development team in the US, hiring and mentoring top talent across Robotics and ML.\n Deliver autonomy capabilities tailored to road conditions and driving norms, in close collaboration with central Autonomy teams.\n Drive full-cycle development: from identifying local autonomy needs, to designing, implementing, testing, and deploying features into production.\n Ensure the team upholds Wayve’s high engineering standards, while operating with agility and independence.\n Work closely with OEM partners in the US — representing Wayve’s autonomy team in technical discussions, capturing product requirements, and shaping joint development plans.\n Establish close working relationships with our product and vehicle operations teams in the US.\n \n About you  \n 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.\n Essential \n \n A strong background in robotics and autonomy, with experience building and deploying systems that operate in real-world environments.\n Demonstrated ability to lead and grow high-performing engineering teams, ideally in geographically distributed or independent settings.\n Comfortable with ambiguity: you can define goals, carve out roadmaps, and deliver high-impact work with minimal supervision.\n Broad technical fluency: capable of reviewing and guiding work across software engineering, ML, controls, and systems integration.\n Excellent communication skills: you’re able to clearly convey technical context and strategic vision across cultures and time zones.\n Strong product sense and stakeholder management skills: you’re comfortable interfacing directly with OEM customers and representing engineering in external-facing conversations. \n \n Desirable \n \n Prior experience in autonomous vehicles or robotic systems operating at scale.\n Familiarity with US's road environment, driving behaviour, or AV regulatory landscape.\n A strong foundation in machine learning and its application to real-time decision-making or perception systems.\n \n  \n 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.\n 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.  \n #LI-KM1 \n Wayve is committed to creatin","salary_min":336400,"salary_max":381600,"location":"Detroit","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["robotics","generative-ai","autonomous-vehicles","machine-learning"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8568364002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T16:04:33Z","expires_at":"2026-06-29T14:12:45.00829Z","created_at":"2026-05-30T14:12:45.119737Z","updated_at":"2026-05-30T14:12:45.119737Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e0e00a39-c16a-434f-bfa5-59174ea0c816"},{"id":"f31b0853-bd93-4641-84d1-bd9c12ae40a7","company_id":"9bad7e3a-74e6-4dae-87c5-f3e9f0e72bd0","title":"Software Engineer, ML Performance Optimization","slug":"software-engineer-ml-performance-optimization-f0fa3daa","description":"Zoox is on a mission to reimagine transportation and ground-up build autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone. We are still in the early stages of deploying our robotaxis on public roads, and it is a great time to join Zoox and have a significant impact in executing this mission. The ML Platform team at Zoox plays a crucial role in enabling innovations in ML and CV to make autonomous driving as seamless as possible. \n \nThe Opportunity\nAre you excited to lead our ML Performance Optimization initiatives and make our Training and Inference platform that enables autonomous driving as fast and efficient as possible? You will get to work across all ML teams within Zoox - Perception, Prediction, Planner, Simulation, Collision Avoidance, and Advanced Hardware Engineering group and have the opportunity to significantly push the boundaries of how ML is practiced within Zoox.\n \nWe build and operate the base layer of ML tools, deep learning frameworks, and inference systems used by our applied research teams for in- and off-vehicle ML use cases. You will lead a team of strong software engineers and act as a force multiplier for our internal customers. This team has a lot of growth opportunities as we expand our robotaxi deployments and venture into new ML domains. If you want to learn more about our stack behind autonomous driving, please look here.\n","salary_min":192000,"salary_max":257000,"location":"Foster City, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","deep-learning","machine-learning","infrastructure"],"apply_url":"https://jobs.lever.co/zoox/bc11276c-8db7-426e-9d00-d41c2097723a/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T22:34:49.506Z","expires_at":"2026-06-29T14:05:50.532577Z","created_at":"2026-05-29T14:17:35.896107Z","updated_at":"2026-05-30T14:05:50.639807Z","company_name":"Zoox","company_slug":"zoox","company_logo_url":"https://www.google.com/s2/favicons?domain=zoox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f31b0853-bd93-4641-84d1-bd9c12ae40a7"},{"id":"152a9a3c-a62a-4f20-a505-60c62517b468","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Machine Learning Scientist, Multimodal AI ","slug":"machine-learning-scientist-multimodal-ai-e50612bb","description":"POSITION SUMMARY: \n Natera is hiring a Machine Learning Scientist to join our AI and computational biology team. This role develops and deploys deep learning models across digital pathology, genomics, transcriptomics, and cell-free DNA (cfDNA) modalities. You will build multimodal AI systems that integrate imaging, molecular, and clinical data, leveraging proprietary genomic and clinical datasets. You will collaborate with scientists, pathologists, bioinformaticians, and software engineers to scale machine learning approaches that advance personalized oncology diagnostics and tumor-informed minimal residual disease (MRD) testing.\n PRIMARY RESPONSIBILITIES: \n \n Design, implement, and evaluate deep learning models across biomedical data modalities, including histopathology imaging, genomic sequencing, transcriptomics, and cfDNA features\n Develop multimodal AI architectures that integrate H\u0026E whole-slide imaging data with molecular and clinical data sources\n Build scalable, production-quality machine learning workflows and pipelines using cloud infrastructure (AWS)\n Apply modern machine learning techniques including convolutional neural networks (CNNs), vision transformers (ViTs), sequence transformers, representation learning, and foundation model fine-tuning\n Collaborate across technical and clinical teams to translate machine learning prototypes into validated tools\n Analyze model outputs to generate reproducible biological and clinical insights\n Document pipelines thoroughly and communicate data-driven findings clearly to cross-functional stakeholders\n \n QUALIFICATIONS: \n \n PhD in Computer Science, Computational Biology, Biomedical Engineering, Bioinformatics, Statistics, or a related quantitative discipline with a focus on machine learning or AI\n Core experience developing machine learning models for biomedical applications, specifically in medical imaging, computational pathology, genomics, transcriptomics, multi-omics, or molecular diagnostics\n Hands-on expertise with PyTorch and strong production-level programming skills in Python\n Practical application of deep learning architectures such as CNNs, transformers, attention mechanisms, and representation learning\n Experience managing datasets and training workflows within distributed or cloud computing environments (AWS)\n Proven ability to take ownership of research projects and translate prototypes into robust, deployment-ready workflows\n Experience adapting pre-trained foundation models for downstream biomedical applications\n \n PREFERRED QUALIFICATIONS: \n \n Experience integrating imaging, molecular, and clinical data within unified multimodal machine learning frameworks\n Technical familiarity with DNA sequencing, RNA sequencing, methylation, and ctDNA assays\n Hands-on experience with digital pathology software and whole-slide imaging analysis\n Exposure to survival modeling, longitudinal prediction, or time-to-event modeling\n Experience applying self-supervised learning, weakly supervised learning, or multiple instance learning (MIL) to clinical data\n Domain knowledge in oncology, biomarker discovery, or clinical precision medicine\n Track record of peer-reviewed publications in machine learning or computational biology conferences and journals (e.g., NeurIPS, ICML, CVPR, MICCAI, Nature Biomedical Engineering)\n \n #LI-DNI\n  \n The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years \u0026 depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.\n Remote USA\n $124,800 — $156,000 USD \n OUR OPPORTUNITY \n Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.\n The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.\n WHAT WE OFFER \n Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!\n For more informatio","salary_min":124800,"salary_max":156000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"mid","tags":["deep-learning","pytorch","healthcare","fine-tuning","generative-ai","cloud","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6004385004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T18:47:01Z","expires_at":"2026-06-29T14:10:20.275908Z","created_at":"2026-05-29T14:38:23.474911Z","updated_at":"2026-05-30T14:10:20.386097Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/152a9a3c-a62a-4f20-a505-60c62517b468"},{"id":"afcbd32a-2f1d-4a75-9315-8bcd1e76ad5e","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Fulfillment Planning","slug":"staff-machine-learning-engineer-fulfillment-planning-8c6dac71","description":"About the Team \n The Fulfillment Planning team builds the intelligence that powers DoorDash’s logistics network. We optimize how deliveries are planned and executed across the full delivery lifecycle, improving customer experience, merchant outcomes, Dasher efficiency, and DoorDash profitability.  Our mission is to improve fulfillment quality while reducing fulfillment cost. We do this by applying machine learning, optimization, and systems engineering to the core decisions behind assignment, routing, batching, timing, and fulfillment estimation.\n The team works on some of DoorDash’s most important logistics systems, including:\n \n The core assignment engine that matches deliveries with Dashers in real time.\n Real-time ETA and fulfillment estimation systems for consumers, Dashers, and merchants across diverse geographies and all business lines.\n Assignment and planning algorithms for specialized delivery types, including grocery, retail, parcel, and catering.\n ML models and optimization algorithms that shape demand, improve service quality, and reduce cost.\n Tier-0 logistics services that require high reliability, low latency, and strong operational discipline.\n \n The team also builds reusable ML systems and modeling patterns that scale across DoorDash’s logistics ecosystem. This role will help define the technical direction and best practices for logistics ML at DoorDash.\n About the Role \n We’re looking for a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems that drive real-time decisioning across DoorDash’s fulfillment ecosystem.\n You will start by owning ML systems for assignment and fulfillment estimation, partnering closely with Product, Data Science, Engineering, and Platform teams to improve delivery quality, cost, and efficiency. Over time, you may also contribute to adjacent areas such as batching, fulfillment execution, demand shaping, and logistics optimization across DoorDash’s business lines.\n This is a high-impact individual contributor role for someone who enjoys building 0→1 ML systems, operating at Staff-level scope, and influencing technical direction across multiple teams. You will define architectures, set modeling and deployment standards, mentor other engineers, and help shape how DoorDash applies machine learning to logistics at scale.\n You’re excited about this opportunity because you will… \n \n Own and build foundational ML systems that directly impact delivery quality, cost, and overall logistics efficiency across DoorDash.\n Work on challenging, real-world machine learning problems , including real-time assignment, routing, and fulfillment estimation.\n Lead 0→1 ML initiatives , defining how machine learning and optimization are applied across fulfillment products.\n Influence architecture, strategy, and execution for a Tier-0 service critical to DoorDash’s logistics platform.\n Collaborate closely with Product, Data Science, and Platform Engineering in a highly cross-functional environment.\n Establish best practices for model development, deployment, monitoring, retraining, and governance.\n Define and lead DoorDash’s cutting-edge AI vision for logistics: an LLM-inspired foundation model for intelligence across logistics\n Mentor other engineers and raise the technical bar for logistics ML across the organization.\n \n We’re excited about you because… \n \n You have 8+ years of industry experience building and deploying production-scale machine learning systems.\n You have strong machine learning fundamentals and know how to apply them to large-scale production systems.\n You are fluent in Python\n You have hands-on experience with modern ML frameworks, especially deep learning frameworks.\n You have designed, launched, and operated mission-critical ML models or systems in production, including monitoring, retraining, reliability, and governance.\n You can lead complex technical projects end to end and influence stakeholders across multiple teams or organizations.\n You communicate clearly with both technical and non-technical audiences.\n You are comfortable operating in ambiguous problem spaces and turning 0→1 ideas into production systems.\n You have built or shipped large-scale ML models for recommendation, ads, marketplace, logistics, or other domains.\n You have experience with knowledge distillation from large teacher models into efficient production models.\n \n  \n Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only\n We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.\n The Covey tool has been reviewed ","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["llm","fine-tuning","generative-ai","cloud","healthcare","deep-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/7962110","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T23:47:57Z","expires_at":"2026-06-29T14:18:34.57356Z","created_at":"2026-05-28T14:20:10.032116Z","updated_at":"2026-05-30T14:18:34.681457Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/afcbd32a-2f1d-4a75-9315-8bcd1e76ad5e"},{"id":"23d134c7-f2bf-4c83-87f8-3938851bc707","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-583097ba","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n ABOUT THE TEAM\n Anduril's Air \u0026 Missile Defense Radar team develops cutting-edge tracking algorithms and software systems that detect, track, and characterize airborne threats in real-time. We're building the next generation of tracking intelligence capabilities—automated analysis systems that understand tracking performance, identify failure modes, and continuously improve our algorithms through data-driven insights.\n This role sits at the intersection of ML engineering and tracking domain expertise. You'll build end-to-end pipelines that ingest tracking algorithm telemetry, analyze correlation failures and performance anomalies, train models to automate root cause analysis, and deploy production tools that help engineers ask questions like \"why didn't track X and track Y associate?\" We don't just track targets; we track our tracking systems and make them smarter.\n WHAT YOU'LL DO\n \n Own tracking intelligence infrastructure end-to-end : Build the platform for ingesting tracking algorithm telemetry (hypotheses, scores, gains, association decisions), feature engineering performance metrics, training analysis models, and deploying them into production\n Automate tracking analysis : Develop ML models that identify correlation failures, track quality degradation, and root causes for tracking anomalies—replacing manual deep-dive investigations with scalable automated insights\n Build autotuning capabilities : Create systems that recognize incoming data characteristics and automatically adjust tracking algorithm parameters, frame rates, and model configurations for optimal performance\n Design human-in-the-loop tools : Build interfaces and query services that let engineers ask natural questions about tracking behavior and get data-driven answers backed by your models\n Exploit tracking telemetry : Instrument C++ tracking algorithms with appropriate logging (working with platform engineers), then marshal that data into consistent formats for analysis and model training\n Deploy in constrained environments : Package and deploy models for air-gapped systems with no external connectivity, following security scanning requirements where ML models are treated as data artifacts\n Manage the ML lifecycle : Handle data catalogs, ground truth labeling, model registries, versioning, and validation—ensuring models improve tracking performance in measurable ways\n Bridge domains : Translate between tracking algorithm fundamentals (Kalman filters, data association, multi-hypothesis tracking) and ML/data science techniques to build solutions that actually work\n Drive make/build decisions : Evaluate when to build custom models vs. leverage existing ML capabilities, selecting appropriate algorithm architectures for tracking intelligence problems\n Work hands-on-keyboard : This is a one-person show initially—you'll architect, code, deploy, and iterate rapidly using modern Python-based ML tooling\n \n REQUIRED QUALIFICATIONS\n \n 3+ years of experience with a strong mix of ML engineering and data science—you've built models AND deployed them into production systems\n Proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, scikit-learn)\n Experience with MLOps practices: data pipelines, feature engineering, model versioning, experiment tracking, and deployment workflows\n Familiarity with ML infrastructure tooling (MLflow, Dagster/Airflow, or similar orchestration tools)\n Understanding of tracking, estimation, or filtering algorithms (Kalman filters, data association techniques)—you need to understand what tracking algorithms output and why they make the decisions they do\n Ability to work with streaming time-series data and engineer features from algorithm telemetry\n Experience building data catalogs, managing ground truth labels, and validating model performance\n Strong software engineering fundamentals—you can build maintainable, production-quality code independently\n Comfortable working in C++ environments enough to add instrumentation/logging (no deep algorithm development required)\n Ability to obtain and maintain a U.S. Top Secret SCI security clearance\n \n PREFERRED QUALIFICATIONS\n \n Experience deploying ML models in edge, embedded, or air-gapped environments with security constraints\n Background in def","salary_min":165000,"salary_max":218000,"location":"Fort Collins, CO","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["tensorflow","mlops","computer-vision","data-pipeline","pytorch","payments","machine-learning"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5126634007?gh_jid=5126634007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T21:29:38Z","expires_at":"2026-06-29T14:06:48.665653Z","created_at":"2026-05-28T14:08:23.033047Z","updated_at":"2026-05-30T14:06:48.786007Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/23d134c7-f2bf-4c83-87f8-3938851bc707"},{"id":"bb30dd7a-6328-49a9-8992-8ef7d074aff9","company_id":"9f42c3ea-cd86-472e-8b5e-d041b53f16bf","title":"Machine Learning Engineer II","slug":"machine-learning-engineer-ii-2a76ae0d","description":"Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.\n On the Servicing ML team, you will build and improve machine learning and AI systems that automate customer operations such as disputes, returns, fraud, and chargebacks to make the best decisions for Affirm and our customers. You will work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring.\n  \n What you'll do \n - You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.\n - You will build models that automate refunds, getting money back to our customers faster.\n - You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.\n - You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.\n - You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.\n  \n What we look for \n - You have a total of 2+ years of experience as a machine learning engineer\n - Strong Python skills and experience writing production-quality code\n - Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost).\n - Experience building applications with LLM APIs (e.g., OpenAI, Anthropic), including structured extraction, prompt engineering, and orchestration frameworks like LangChain or LangGraph.\n - Familiarity with document and unstructured data processing (PDF/image extraction, text parsing, or similar).\n - Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).\n - Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.\n - You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code.\n - You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.\n - Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.\n - You have strong verbal and written communication skills that support effective collaboration with our global engineering team.\n \n  \n  \n Pay Grade - L Equity Grade - 5 Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills.  Base pay is part of a total compensation package that may include monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents). In addition, the employees may be eligible for equity rewards offered by Affirm Holdings, Inc. (parent company). CAN base pay range per year: $125,000 - $175,000 \n Location - Remote Canada\n #LI Remote\n \n Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.\n We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:  \n \n Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents  \n Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses \n Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge \n ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount \n \n We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We are happy to provide reasonable accommodations to candidates in need of individuali","salary_min":125000,"salary_max":175000,"location":"Remote (Canada)","workplace":"remote","job_type":"full-time","experience_level":"junior","tags":["mlops","agents","llm","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/affirm/jobs/7719653003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-25T15:10:02Z","expires_at":"2026-06-29T14:17:59.886987Z","created_at":"2026-05-27T14:18:51.302019Z","updated_at":"2026-05-30T14:18:00.002246Z","company_name":"Affirm","company_slug":"affirm","company_logo_url":"https://www.google.com/s2/favicons?domain=affirm.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bb30dd7a-6328-49a9-8992-8ef7d074aff9"},{"id":"9bb36624-5eb3-472c-87cb-a72da24480bc","company_id":"9f42c3ea-cd86-472e-8b5e-d041b53f16bf","title":"Machine Learning Engineer II","slug":"machine-learning-engineer-ii-24c947be","description":"Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest.\n On the Servicing ML team, you will build and improve machine learning and AI systems that automate customer operations such as disputes, returns, fraud, and chargebacks to make the best decisions for Affirm and our customers. You will work closely with experienced ML engineers, platform partners, and cross-functional stakeholders to take models from idea to prototype to production, and to keep them healthy with strong measurement and monitoring.\n  \n What you'll do \n - You will develop AI systems that automate dispute and chargeback handling using structured evidence and business logic, creating a better experience for our customers.\n - You will build models that automate refunds, getting money back to our customers faster.\n - You will build and maintain evidence extraction pipelines that process unstructured data using LLM-powered workflows to produce structured, actionable outputs.\n - You will prototype new modeling ideas, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls.\n - You will collaborate across Engineering, Servicing Operations, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences.\n  \n What we look for \n - You have a total of 2+ years of experience as a machine learning engineer\n - Strong Python skills and experience writing production-quality code\n - Experience building and evaluating models for tabular classification problems (preferably gradient-boosted decision trees like LightGBM/XGBoost/CatBoost).\n - Experience building applications with LLM APIs (e.g., OpenAI, Anthropic), including structured extraction, prompt engineering, and orchestration frameworks like LangChain or LangGraph.\n - Familiarity with document and unstructured data processing (PDF/image extraction, text parsing, or similar).\n - Experience with ML lifecycle tooling for training orchestration, experimentation, and model monitoring (e.g., Kubeflow, Airflow, MLflow, or equivalent internal platforms).\n - Proficient in using AI-powered developer tools (e.g., Claude Code, Cursor, or similar) to accelerate iteration, debugging, and code quality as part of day-to-day development workflows.\n - You have mastered taking a simple problem or business scenario into a solution that interacts with multiple software components, and executing on it by writing clear, easily understood, well tested and extensible code.\n - You are comfortable navigating a large code base, debugging others' code, and providing feedback to other engineers through code reviews.\n - Your experience demonstrates that you take ownership of your growth, proactively seeking feedback from your team, your manager, and your stakeholders.\n - You have strong verbal and written communication skills that support effective collaboration with our global engineering team.\n - This position requires either equivalent practical experience or a Bachelor’s degree in a related field\n  \n \n Base Pay Grade - L Equity Grade - 6\n Employees new to Affirm typically come in at the start of the pay range. Affirm focuses on providing a simple and transparent pay structure which is based on a variety of factors, including location, experience and job-related skills. Base pay is part of a total compensation package that may include equity rewards, monthly stipends for health, wellness and tech spending, and benefits (including 100% subsidized medical coverage, dental and vision for you and your dependents.) USA base pay range (CA, WA, NY, NJ, CT) per year: $160,000 - $210,000 USA base pay range (all other U.S. states) per year: $142,000 - $192,000 #LI-Remote\n  \n \n Affirm is proud to be a remote-first company! The majority of our roles are remote and you can work almost anywhere within the country of employment. Affirmers in proximal roles have the flexibility to work remotely, but will occasionally be required to work out of their assigned Affirm office. A limited number of roles remain office-based due to the nature of their job responsibilities.\n We’re extremely proud to offer competitive benefits that are anchored to our core value of people come first. Some key highlights of our benefits package include:  \n \n Health care coverage - Affirm covers all premiums for all levels of coverage for you and your dependents  \n Flexible Spending Wallets - generous stipends for spending on Technology, Food, various Lifestyle needs, and family forming expenses \n Time off - competitive vacation and holiday schedules allowing you to take time off to rest and recharge \n ESPP - An employee stock purchase plan enabling you to buy shares of Affirm at a discount \n \n We believe It’s On Us to provide an inclusive interview experience for all, including people with disabilities. We a","salary_min":142000,"salary_max":192000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"junior","tags":["agents","mlops","llm","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/affirm/jobs/7719651003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-25T15:10:00Z","expires_at":"2026-06-29T14:17:59.968032Z","created_at":"2026-05-27T14:18:51.394226Z","updated_at":"2026-05-30T14:18:00.078113Z","company_name":"Affirm","company_slug":"affirm","company_logo_url":"https://www.google.com/s2/favicons?domain=affirm.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9bb36624-5eb3-472c-87cb-a72da24480bc"},{"id":"c96f95a6-0aa8-42c2-9fd5-75a8b7173a25","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Principal Machine Learning Engineer- LLM Fine-tuning and Optimization ","slug":"principal-machine-learning-engineer-llm-fine-tuning-and-optimization-bfee7362","description":"Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. \n Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.\n The Community You Will Join:  \n Machine Learning and Artificial Intelligence are at the heart of the Airbnb product. From Trust to Payments, and from Customer Service to Marketing we rely on ML to ensure that guests and hosts have the best possible experience with Airbnb. \n The CS AI product team is responsible for driving CSxAI (Customer Support x Artificial Intelligence) initiatives by adopting the Generative AI technologies to enable an intelligent, scalable and exceptional service experience. The team develops and enhances various AI models, ML services and tools including LLM fine-tuning, alignment and optimization, RAG/Search,  LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb. \n What you will do: \n As a principal machine learning engineer, you will be responsible for fine-tuning state-of-the-art LLMs for diverse use cases while optimizing models for high-performance deployment on Airbnb’s ML Infrastructure. You will partner with product managers, software engineers, data scientists and operation teams to brainstorm, design and develop AI products such as AI Assistant, Autonomous agent,  recommendation, travel planning, and many more products that make meaningful impacts in the world of travel. \n Your responsibilities:  \n \n Work with large scale structured and unstructured data; explore, experiment, build and continuously improve foundation models for Airbnb product, business and operational use cases.\n Create a multi-year tech roadmap that enables our team to stay on the leading edge of the rapidly evolving AI landscape and leverage the best in class technologies to deliver customer benefits.\n Continuously evaluate recent and upcoming large foundational models, ensuring the selection and refinement of the highest quality models for enhanced performance and efficiency.\n Hands-on prototype, develop and productionize LLM models and pipelines at scale, including both batch and real-time use cases.\n Drive key AI architectural decisions for products, and contribute to Airbnb’s ML platform architecture and strategy.\n \n Minimum Qualifications :\n \n PhD in Computer Science,  Machine Learning, Mathematics, Statistics, or related technical field.\n 10+ years of experience with developing machine learning models and products at scale from inception to business impact.\n Programming experience in Python and hands-on experience with frameworks such as PyTorch.\n Proven record of training, fine tuning, optimizing models and inference run-time\n Post-training experience in areas like data processing for fine-tuning; responsible LLMs; LLM alignment; reinforcement learning; efficient training and inference; language model evaluation; and/or multilingual and multimodal modeling.\n Or specialized experience in runtime optimizations, model quantization, compression, on-device inference, GPU inference, pytorch, kernel development\n \n Preferred Qualifications: \n \n PhD in AI, machine learning, data science, or related technical fields.\n \n Publications at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL). \n \n Customer Support Systems : Experience with AI technologies in customer support applications.\n Agile Practice for AI production : Experience with the entire AI product development lifecycle from incubation to production at scale, following agile practices in the Applied AI/ML domain.\n \n \n Infrastructure Acumen : Experience deploying and scaling business-critical AI services and driving architectural requirements on ML infrastructures\n \n Your Location: \n This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity.  Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list . If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. \n Our Commitment To Inclusion \u0026 Belonging: \n Airbnb is committed to working with the broadest talent","salary_min":292000,"salary_max":365000,"location":"United States","workplace":"onsite","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","fine-tuning","pytorch","generative-ai","llm","payments","agents","machine-learning"],"apply_url":"https://careers.airbnb.com/positions/7955579?gh_jid=7955579","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-24T23:37:28Z","expires_at":"2026-06-29T14:09:02.019884Z","created_at":"2026-05-27T14:09:19.150599Z","updated_at":"2026-05-30T14:09:02.131762Z","company_name":"Airbnb","company_slug":"airbnb","company_logo_url":"https://www.google.com/s2/favicons?domain=airbnb.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c96f95a6-0aa8-42c2-9fd5-75a8b7173a25"},{"id":"a7e6a301-28c1-4bf3-b2d5-149f49ee0273","company_id":"9b9ed348-97e6-4bb8-987a-d4db92c3949a","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-084fb6cb","description":"What’s in it for you?  \n Ready to make a serious impact? Millions of people already rely on Calendly, and we’re still in the midst of exciting product growth — it’s a fantastic time to join us. Everything you’ll work on here will accelerate your career to the next level. If you want to learn, grow, and do the best work of your life alongside the best people you’ve ever worked with, then we hope you’ll consider allowing Calendly to be a part of your professional journey.\n About the team \u0026 opportunity  \n What’s so great about working on Calendly’s Data Science \u0026 Machine Learning team? \n We make things possible for our customers through innovation in data, analytics and AI.\n Why do we need you? Well, we are looking for a Machine Learning Engineer who will deliver business value by building new AI Products for Calendly’s customers. You will report to the head of AI and will be responsible for building and operating AI-powered features that create magical experiences for our customers.\n Our team:\n \n Builds new AI products for Calendly’s customers, bringing new capabilities to life.\n Works closely with product, design, marketing, customer success, and engineering teams to implement AI solutions to real customer problems.\n Drives innovation and makes an impact.\n \n You will join a high performing AI team and be an integral part of building new, AI based experiences for internal and external customers alike.\n What you’ll do \n On a typical day, you’ll own features end to end within our AI ecosystem, with growing independence and impact.\n \n Own AI powered features from design through deployment, partnering with product, design, and engineering to scope work and define success metrics.\n Understand and share domain knowledge, answering domain specific questions for your product area and documenting what you learn for the team.\n Prioritize your work independently, balancing feature development, quality, and maintenance, and communicating tradeoffs clearly.\n Proactively seek and offer support to teammates pairing, reviewing, and collaborating to move projects forward.\n Understand and troubleshoot our deployment pipelines, including build, test, and release steps for ML services and data pipelines.\n Use our monitoring and observability tools to effectively triage alerts and incidents, collaborating with partners to restore service and prevent recurrence, and participate in the team’s on-call rotation and incident response. \n Serve as a subject matter expert for the features and services you own, including their data contracts, SLAs, and dependencies.\n Be a frequent user of AI Tools and champion of adoption to the rest of the company.  \n \n  \n What do we need from you? \n \n 6+ years of industry experience in applied AI/ML and software engineering with a demonstrated track record of shipping and operating AI /ML applications in production.\n Experience working on GenAI products architecture/system design and implementation. \n Hands-on experience implementing AI/ML solutions for high-traffic, low-latency, large-data applications that produced tangible impact for end users.\n Understanding of foundation models and the open-source ecosystem, including model fine-tuning and prompt engineering for real product use cases.\n Strong programming (Python / Scala / Java / SQL etc) and data engineering skills.\n High level of ownership, and the ability to find a way to success. \n You have strong verbal and written communication skills. \n Authorized to work lawfully in the United States of America as Calendly does not engage in immigration sponsorship at this time  \n \n What’s in it for you?  \n Ready to make a serious impact? Millions of people already rely on Calendly’s products, and we’re still in the midst of our growth curve — it’s a fantastic time to join us. Everything you’ll work on here will accelerate your career to the next level. If you want to learn, grow, and do the best work of your life alongside the best people you’ve ever worked with, then we hope you’ll consider allowing Calendly to be a part of your professional journey. \n Our Hiring Process: \n We aim to provide an inclusive and equitable candidate experience to everyone who expresses interest in working at Calendly. To learn more about our hiring process, please visit our careers page at www.careers.calendly.com .\n Once selected for an opportunity, the recruiter assigned to the role will keep you informed every step of the way. Have questions? Let your recruiter know! Want to share your experience? We are passionately committed to improving and building on our process, and we consider feedback a gift. \n If you are an individual with a disability and would like to request a reasonable accommodation as part of the application or recruiting process, please contact us at recruiting@calendly.com .  \n Calendly is registered as an employer in many, but not all, states. If you are located in Alaska, Alabama, Delaware, Hawaii","salary_min":198025,"salary_max":239960,"location":"Remote (US)","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","data-pipeline","generative-ai","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/calendly/jobs/8563197002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T22:58:30Z","expires_at":"2026-06-29T14:18:10.748008Z","created_at":"2026-05-27T14:19:02.542405Z","updated_at":"2026-05-30T14:18:10.860952Z","company_name":"Calendly","company_slug":"calendly","company_logo_url":"https://www.google.com/s2/favicons?domain=calendly.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a7e6a301-28c1-4bf3-b2d5-149f49ee0273"},{"id":"d134135d-62d9-4aa9-acb7-410bbd77911c","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Sr. Staff Machine Learning Engineer, Content Ecosystem","slug":"sr-staff-machine-learning-engineer-content-ecosystem-3ffaf377","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n Pinterest works when the content ecosystem works: when people can reliably find ideas that feel inspiring, trustworthy, and actionable—and when the ecosystem continuously learns what to create, surface, and sustain next. In this Sr. Staff ML Engineer role, you’ll be the technical lead shaping how Pinterest understands and improves its content as a living marketplace: a dynamic system with feedback loops between users, creators/publishers, distribution, and long-term business outcomes.\n You will define a durable ML strategy that goes beyond “engagement metrics” to improve overall ecosystem health—identifying where we’re underserving content, uncovering the attributes that make content succeed, and designing optimization approaches that balance relevance, quality, diversity, integrity, and monetization. The problems are inherently multi-objective and long-horizon: the best decisions today should strengthen the ecosystem tomorrow. If you’re excited by high-leverage technical leadership, rigorous ML thinking, and marketplace-style dynamics at scale, this role offers a chance to directly shape Pinterest’s moat and the experience millions of people come to for ideas they can act on.\n What you’ll do: \n \n Set technical strategy and vision for ML systems that improve the end-to-end content ecosystem, including supply, distribution, and engagement/utility outcomes.\n Partner with DS teams to develop a content ecosystem measurement framework to quantify content health and performance (e.g., content quality, freshness, diversity, coverage, creator/content sustainability, and user value), and align it with company/business goals.\n Identify and close content gaps by building models and insights that answer: what content is missing, for whom, in which contexts, and why.\n Deeply understand what content works and why by combining causal thinking, experimentation, and model interpretability to connect content attributes and distribution mechanisms to downstream user and business outcomes.\n Build and optimize content marketplace mechanisms that balance multi-sided incentives and constraints (e.g., users, creators/publishers, advertisers, internal policy/safety), while maximizing long-term ecosystem value.\n Design multi-objective optimization approaches that manage tradeoffs across relevance, quality, diversity, creator incentives, integrity/safety, and monetization.\n Partner closely with cross-functional teams (Product, Data Science, UX Research, Content/Creator teams, Trust \u0026 Safety, Ads, Infra) to translate ambiguous ecosystem problems into clear technical roadmaps and deliver measurable impact.\n Mentor and grow junior ML engineers through technical coaching, design reviews, career development support, and creating a culture of strong engineering and scientific rigor.\n Raise the quality bar for ML engineering by establishing best practices for data quality, model governance, reliability, privacy-aware design, and operational excellence.\n Communicate clearly and influence broadly by producing crisp technical proposals, aligning stakeholders on tradeoffs, and driving decisions across org boundaries.\n Explore and apply advanced methods where beneficial—e.g., game-theoretic approaches, reinforcement learning, mechanism design, or bandit-style optimization—to improve marketplace dynamics and long-term ecosystem outcomes.\n \n What we’re looking for: \n \n Strong fundamentals in machine learning and optimization, with the ability to apply them to real-world, high-scale ecosystem problems.\n Demonstrated ability to lead technical strategy, navigate ambiguity, and deliver end-to-end impact.\n Deep interest in marketplace dynamics (multi-sided incentives, feedback loops, long-term health metrics), and comfort with multi-objective tradeoffs.\n Experience with Cursor, Copilot, Codex, or","salary_min":227871,"salary_max":469147,"location":"San Francisco, CA","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","code-generation","reinforcement-learning","llm","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=7919043","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T21:44:36Z","expires_at":"2026-06-29T14:08:27.825754Z","created_at":"2026-05-27T14:08:41.489689Z","updated_at":"2026-05-30T14:08:27.939307Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d134135d-62d9-4aa9-acb7-410bbd77911c"},{"id":"4ec47e11-41e8-4449-b012-74d40f99df46","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Staff Machine Learning Engineer, Ads Conversion","slug":"staff-machine-learning-engineer-ads-conversion-06f85a08","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n We are looking for a Staff MLE to lead the technical vision for our Ads Advanced Conversion Modeling team, building the state-of-the-art systems that power our global marketplace.\n  \n What you’ll do:  \n \n Lead the technical direction and development of state-of-the-art applied ML projects for ads conversion.  \n Design and build large-scale DNN models to improve user action prediction with low latency.  \n Mine text, visual, and user signals to better understand intention and infer interests from online activity.  \n Use AI to accelerate analysis and iteration, while applying judgment and verification to ensure correctness and quality.  \n Automate repeatable tasks such as documentation, reporting, and QA checks to speed up the development lifecycle.  \n Coach and mentor engineers while collaborating with product and sales to design new ad products.\n \n  \n What we’re looking for: \n \n Degree in Computer Science, Statistics, or a related field.  \n 6+ years of industry experience building production ML systems at scale (Search, Recommendations, or Ranking).  \n 2+ years of experience leading technical projects or teams.  \n Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs.  \n Experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.\n Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.\n High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final deliverables.\n Strong mathematical foundation and experience with statistical methods and A/B testing. \n \n  \n Relocation Statement:  \n \n This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.\n \n  \n In-Office Requirement Statement: \n \n We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.\n This role will need to be in the office for in-person collaboration 1-2 times every 6 months and therefore can be situated anywhere in the country.\n \n  \n #LI-SM4\n #LI-REMOTE\n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.\n Information regarding the culture at Pinterest and benefits available for this position can be found here . \n US based applicants only\n $222,716 — $389,753 USD \n Our Commitment to Inclusion: \n Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete  this form  fo","salary_min":222716,"salary_max":389753,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","llm","code-generation","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=7902034","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T19:35:37Z","expires_at":"2026-06-29T14:08:28.182671Z","created_at":"2026-05-27T14:08:41.839976Z","updated_at":"2026-05-30T14:08:28.299621Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4ec47e11-41e8-4449-b012-74d40f99df46"},{"id":"1598afd6-7ff3-417e-b3a0-138ecb576a46","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"Machine Learning Scientist","slug":"machine-learning-scientist-de3cf41b","description":"Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.  \n At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.  \n A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. \n WHY DATA SCIENCE AND ANALYTICS \n The Data Science \u0026 Analytics organization's mission is to increase our speed, frequency, and acumen in making decisions at scale by instilling a data-influenced approach to building products. We cover a wide area of the data spectrum, including analytical data engineering, product analytics, experimentation, causal inference, statistical modeling, and machine learning. Aligned and partnering with product verticals, we use this extensive tool belt to discover new opportunities and unmet use cases, influence and shape the product roadmap and prioritization, build data products, and measure impact on our community of players and developers.\n In Data Science \u0026 Analytics, you will contribute to horizontal ML systems and infrastructure that enable us to understand the trajectory of users and creators as well as the overall business to inform investment opportunities and accelerate our growth. These systems inform experiment decision making, product roadmaps, and execution risk on our road to connecting one billion users. \n You Will:  \n \n Develop, build, and support large scale forecasting systems for business growth\n Develop, build, and support modeling of long-term user outcomes\n Design and implement batch prediction infrastructure that ensures high-levels of accuracy, provides explainability, and quantifies uncertainty\n Collaborate with data science and product partners to unlock causal understanding of our business growth and to develop scalable solution for measuring ecosystem health\n Model and promote a high bar for technical excellence in the broader data science and ML community.\n Communicate strategic findings to influence company and team-level roadmaps\n Partner with Data Engineering and Data Platform teams to ensure model development, reporting, and monitoring systems are built in a reliable and robust way. \n \n You Have:  \n \n 5+ years of industry experience in prototyping and building scalable machine learning solutions.\n Experience building scalable and robust ETL data and ML pipelines with complex upstream dependencies and accountability for downstream consumers\n Experience with time series modeling in practice and theory. Experience with foundational time series models (i.e. transformer based) and fine tuning frameworks.\n Demonstrated ability to lead project areas from scratch, and break product requirements into iterative deliverable stages.\n Strong communication skills to connect model outputs to company-level strategy and to integrate horizontal solutions into vertical team operations and systems.\n An Advanced Degree (MSc or PhD) or equivalent degree in Statistics, Economics, Operations Research, Computer Science, Applied Math, Physics, Engineering, or other quantitative fields.\n For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page .\n Annual Salary Range\n $263,670 — $322,820 USD \n Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).\n Roblox 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. Roblox also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process.\n For US based roles only, please note the Company may not be able to employ candidates for this role who have United","salary_min":263670,"salary_max":322820,"location":"San Mateo, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","data-pipeline","machine-learning","data-science"],"apply_url":"https://careers.roblox.com/jobs/7950872?gh_jid=7950872","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T03:02:22Z","expires_at":"2026-06-29T14:17:02.317307Z","created_at":"2026-05-27T14:17:50.446567Z","updated_at":"2026-05-30T14:17:02.42643Z","company_name":"Roblox","company_slug":"roblox","company_logo_url":"https://www.google.com/s2/favicons?domain=roblox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1598afd6-7ff3-417e-b3a0-138ecb576a46"},{"id":"ffc677b3-ea58-4292-a9a8-ffc5cf009a40","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"Senior Machine Learning Engineer, GenAI Data","slug":"senior-machine-learning-engineer-genai-data-0eedcadb","description":"Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.  \n At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.  \n A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. \n As a Senior Software Engineer  on the Foundation AI organization, you will sit at the epicenter of our foundation model efforts. While the research world is focused on architecture, you will be the architect of the data flywheel that makes VideoGen and 3DGen possible. You aren't just building pipelines; you are building the infrastructure that defines how our models perceive and generate virtual worlds in three dimensions and across time.\n In this role, you will partner directly with our AI researchers to advance beyond experimental datasets and into the realm of dynamic, high-fidelity data synthesis and evaluation. You will bridge the gap between research prototypes working locally to scaling for millions of users. You will design, implement, and scale robust, high-performance infrastructure to crawl, create, curate, store, and serve the massive datasets required for these models. We are seeking accomplished software engineers with a passion for data, experience building large distributed systems, and a commitment to writing high-quality, well-tested code to solve complex data challenges at scale. Your contributions will ensure that our foundation models receive the highest quality data, thereby supporting the next generation of creative AI.\n You will: \n \n High-Scale Data Orchestration: Architect and maintain automated pipelines for the ingestion, cleaning, and pre-processing of multi-modal datasets (video, 3D,) spanning petabytes of data\n Synthetic Data Generation: Leverage image and video generation models to scale multi-modal synthetic datasets\n Research-to-Production Bridge: Partner with research teams to create training data for research experiments – research and implement synthetic data creation pipelines\n Scalable Evaluation Frameworks: Build and own evaluation—automating both heuristic-based metrics and human-in-the-loop interfaces to evaluate and benchmark training datasets and in-house foundation models\n Model Deployment \u0026 API Architecture: Design and optimize high-throughput, low-latency Inference APIs for internal and external consumer access\n Autonomous SOTA Tracking: Actively participate in literature reviews and paper reading groups to identify and implement the latest optimizations in generative modeling\n Resource Efficiency \u0026 Observability: Implement monitoring pipeline health, optimizing data loading to ensure GPUs are used efficiently\n \n You have: \n \n 8+ years of experience as a research-focused data systems engineer (preferably working with 3D and video foundation models)\n Expertise in building scalable ML data pipelines for both batch and real-time environments. Experience working with and processing very large datasets (Petabytes or more).\n Versatile: You're a generalist and you are comfortable with several languages and technologies already; you are adaptable in any situation\n Team-Player \u0026 Technical Leader: You are a collaborative team member who actively mentors peers, drives technical excellence, and takes ownership of leading and delivering key features and projects across team boundaries\n Python Proficiency: You can write high-quality Python code for automation, tooling, and infrastructure management\n Experience with cloud data platforms and distributed processing technologies (e.g., Spark, Ray, Kubeflow, S3, etc.).\n Are passionate about the potential of generative AI, particularly in creative domains like 3D/4D content.\n A Bachelor's degree or equivalent experience in Computer Science, Computer Engineering, or a similar technical field\n \n You are:  \n \n MLOps Experience: Knowledge of experiment tracking (Weights \u0026 Biases, MLflow) and versioning for massive datasets.\n Custom Tooling Development: Experience building internal \"human-in-the-loop\" tools for data labeling specific to video or 3D.\n C++ Knowledge: Optimize the performance of data loaders and being comfortable modifying engine code.\n Game development and digital content creation tools : Experience with making Roblox games, using Blender, Unreal Engine, or Unity.\n  \n For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variet","salary_min":243290,"salary_max":295250,"location":"San Mateo, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","generative-ai","mlops","data-pipeline","machine-learning"],"apply_url":"https://careers.roblox.com/jobs/7943933?gh_jid=7943933","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T23:44:43Z","expires_at":"2026-06-29T14:17:04.294884Z","created_at":"2026-05-27T14:17:52.923084Z","updated_at":"2026-05-30T14:17:04.408385Z","company_name":"Roblox","company_slug":"roblox","company_logo_url":"https://www.google.com/s2/favicons?domain=roblox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ffc677b3-ea58-4292-a9a8-ffc5cf009a40"},{"id":"e0199f32-6837-4dff-a051-e26f020d0530","company_id":"f0c85993-fd53-4228-8ce3-b758284c4ecf","title":"Head of AI and Machine Learning Engineering","slug":"head-of-mlai-engineering-1a544c8b","description":"About Gusto \n At Gusto, we're on a mission to grow the small business economy. We handle the hard stuff — payroll, health insurance, 401(k)s, and HR — so owners can focus on their craft and their customers. With teams in Denver, San Francisco, and New York, we support more than 500,000 small businesses nationwide and are building a workplace that reflects the people we serve.\n   All full-time employees receive competitive base pay, benefits, and equity (RSUs) — because everyone who helps build Gusto should share in its success. Offer amounts are determined by role, level, and location. Learn more about our Total Rewards philosophy .\n   AI is a fundamental part of how work gets done at Gusto. We expect all team members to actively engage with AI tools relevant to their role and grow their fluency as the technology evolves. AI experience requirements vary by role and will be assessed during the interview process.\n About the Role: \n Gusto sits at the center of many of the most important workflows for small businesses, which creates a meaningful opportunity to use rich product and customer data to build AI- and ML-powered systems that improve customer experiences, automate complex work, support better decision-making, and help small businesses thrive. As Gusto becomes more AI-native, we are evolving how AI, ML, risk modeling, and platform capabilities come together across our products and internal systems.\n We are seeking a strategic Head of AI/MLE to lead this next chapter. In this key leadership role, you will define how Gusto builds, deploys, evaluates, and scales AI/ML systems across the company. You will lead a broad organization spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists, while partnering closely with senior business leaders to shape where AI can create durable customer and business impact.\n As the Head of AI/MLE at Gusto, you will be responsible for unifying classical ML and GenAI into a coherent technical strategy, maturing the platform for broader self-service adoption, and shaping how AI-native products and production systems are built at Gusto. Your teams will help translate business problems into end-to-end AI/ML systems, from experimentation and prototyping through evaluation, deployment, monitoring, feedback loops, and operational governance.\n Your leadership will be critical in setting the technical direction, operating model, and quality bar for AI at Gusto. You will help teams move quickly where speed and learning matter most, while ensuring production systems meet high standards for reliability, measurement, safety, and long-term maintainability. This is a senior technical executive role for someone who can combine deep engineering credibility, strong business judgment, and executive-level influence to make AI a durable advantage for Gusto.\n Here’s what you’ll do day-to-day: \n \n Lead, manage, and develop a broad AI/MLE organization spanning Machine Learning Engineering, ML Platform, Risk Data Science, and AI Scientists, fostering a culture of technical excellence, customer impact, collaboration, and continuous learning.\n Define and execute Gusto’s AI/ML systems strategy, unifying classical ML, GenAI, risk modeling, and platform capabilities into a coherent approach that supports Gusto’s broader business and product goals.\n Partner with senior leaders across Product, Engineering, Design, Data, Risk, Legal, Security, and business teams to identify where AI/ML can create meaningful customer value, business impact, and operational leverage.\n Shape how AI-native products and internal systems are built at Gusto, helping teams translate business problems into end-to-end AI/ML systems with clear standards for evaluation, monitoring, observability, reliability, safety, governance, and long-term maintainability.\n Lead the development and maturation of AI/ML platform capabilities, tooling, primitives, guardrails, and deployment patterns that make it easier for product and engineering teams to build, evaluate, deploy, and operate AI/ML systems with less friction, more autonomy, and the right quality bar.\n Drive disciplined technical and business judgment around AI/ML investments, including where to build, where to leverage existing capabilities, and where to avoid unnecessary complexity.\n Create room for fast experimentation and learning where appropriate, while ensuring high-impact production systems meet strong standards for quality, operational rigor, and business accountability.\n Set clear goals, KPIs, and operating rhythms to measure the performance, adoption, and business impact of AI/ML systems, and communicate progress and tradeoffs clearly to senior leadership.\n Stay close to the frontier of AI/ML advancement and help Gusto apply new technologies pragmatically, with strong judgment about what is durable, useful, and ready for production.\n \n Here’s what we're looking for: \n \n We love meeting people with different technical and leaders","salary_min":320000,"salary_max":350000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["generative-ai","payments","llm","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/gusto/jobs/7948318","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T16:28:29Z","expires_at":"2026-06-29T14:09:33.295062Z","created_at":"2026-05-27T14:09:48.374664Z","updated_at":"2026-05-30T14:09:33.417757Z","company_name":"Gusto","company_slug":"gusto","company_logo_url":"https://www.google.com/s2/favicons?domain=gusto.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e0199f32-6837-4dff-a051-e26f020d0530"},{"id":"834a5e1e-decd-411c-9721-220c31f787b8","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Staff Machine Learning Engineer, Embeddings Platform","slug":"staff-machine-learning-engineer-embeddings-platform-6f1a0b06","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n The LS Embedding Machine Learning team is at the forefront of building highly expressive machine learning models that power Reddit’s recommendation systems. We go beyond standard retrieval and ranking architectures, leveraging modern deep learning approaches and scalable model designs to enhance personalization across Reddit’s ecosystem. Our work impacts content discovery, user engagement, and platform growth at a massive scale.\n How You'll Have Impact \n As a Staff Machine Learning Engineer , you will own the technical direction for large-scale machine learning models, guiding the development of advanced deep learning architectures and high-impact ML systems. You will partner with leadership to define ML roadmaps, drive innovation in scalable model design and training approaches, and ensure efficient, reliable deployment of ML models in production. This role offers an opportunity to influence key AI-driven systems across Reddit while mentoring and uplifting the team’s technical capabilities.\n What You’ll Do \n \n Architect and lead the development of next-generation, large-scale machine learning techniques.\n Define and execute the ML strategy, identifying opportunities to enhance personalization and recommendation quality across Reddit.\n Lead research initiatives on scalable machine learning systems and real-time model adaptation, bringing cutting-edge advancements into production.\n Partner with ML infrastructure teams to build high-performance, distributed training systems that efficiently scale across multiple GPUs and cloud environments.\n Establish and optimize real-time serving architectures for large-scale embeddings, ensuring low-latency inference and high throughput.\n Collaborate cross-functionally with teams in Feed Ranking, Ads, Content Understanding, and Core ML to integrate ML models into Reddit’s key AI-driven systems.\n Mentor and guide senior and mid-level ML engineers, fostering a culture of excellence, innovation, and knowledge sharing.\n Stay at the forefront of AI research, evaluating and introducing new modeling paradigms to keep Reddit’s ML ecosystem cutting-edge.\n Drive technical discussions, present findings to leadership, and contribute to long-term ML planning and decision-making.\n \n Who You Might Be: \n \n 8+ years of experience in machine learning engineering, with a strong focus on large-scale ML systems and recommendation or personalization systems.\n Expertise in modern deep learning architectures, including sequence models and foundational models.\n Deep understanding of complex multi-entity relationships in machine learning applications and how they are modeled in large-scale systems.\n Proven ability to design, implement, and optimize scalable ML architectures, from distributed training to real-time inference.\n Strong software engineering skills in Python, C++, or similar languages, with experience in ML infrastructure, high-performance computing, and cloud-based ML pipelines.\n Demonstrated leadership in driving ML strategy, mentoring engineers, and influencing cross-functional teams.\n Experience with A/B testing, model evaluation frameworks, and real-time feedback loops in large-scale production systems.\n Excellent communication skills, with the ability to effectively present complex ML concepts to technical and non-technical stakeholders. \n \n Benefits: \n \n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\n Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support\n Family Planning Support\n Gender-Affirming Care\n Mental Health \u0026 Coaching Benefits\n Flexible Vacation \u0026 Paid Volunteer Time Off\n Generous Paid Parental Leave \n \n #LI-Remote\n Pay Transparency: \n This job posting may span more than one career level.\n In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ .\n To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage","salary_min":253300,"salary_max":354600,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","deep-learning","distributed-systems","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/7867308","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T04:44:46Z","expires_at":"2026-06-29T14:08:32.528235Z","created_at":"2026-05-27T14:08:46.627607Z","updated_at":"2026-05-30T14:08:32.638741Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/834a5e1e-decd-411c-9721-220c31f787b8"},{"id":"31539a5e-cc10-4277-ac39-bd766925c679","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Staff Machine Learning Engineer, Programmatic Ads","slug":"staff-machine-learning-engineer-programmatic-ads-019343aa","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n Pinterest is building a new Programmatic Ads ML team to bring in exchange-sourced ads demand and supply. We’re looking for a Staff ML engineer to develop core bidding and ranking systems that help us optimally buy and sell inventory across exchanges, driving strong ROI for advertisers and growing a critical revenue stream for Pinterest.\n  \n What you’ll do: \n \n Design and implement algorithms for real-time bidding, ad scoring/ranking, inventory selection, and yield optimization across multiple exchanges.\n Own end-to-end ML systems: problem framing, metrics, data/feature design, model training, evaluation, and online experimentation.\n Introduce and productionize new exchange and supply signals (e.g., quality, conversions, identity, fraud, content understanding) to unlock incremental advertiser value.\n Partner closely with Ads Ranking \u0026 Bidding, Measurement, and Programmatic Engineering to integrate new models and objectives into the ads stack.\n Use AI to accelerate analysis, experimentation, and iteration (e.g., exploring model variants, automating path from learnings to launch) while applying strong judgment and vision.\n \n  \n What we’re looking for \n \n Industry experience building and shipping large-scale production ML systems in ads, search, recommendations, or related domains.\n Deep experience with control/optimization algorithms for bidding, pacing, allocation, or similar marketplace problems.\n Strength in probabilistic modeling and measurement (e.g., quality/fraud signals, deep-learning engagement prediction) and making principled trade-offs between coverage, accuracy, and impact.\n Proven Staff-level technical leadership as an IC: driving technical direction and cross-team alignment without formal people management.\n Demonstrated ability to use AI to improve speed and quality of your workflow, with a strong track record of validating and stress-testing AI-assisted outputs.\n Degree in Computer Science, Statistics, or a related field.\n Experience with Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.\n Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.\n \n  \n Relocation Statement :\n \n This position is not eligible for relocation assistance. Visit our  PinFlex page to learn more about our working model.\n \n  \n In-Office Requirement Statement :.\n \n We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.\n This role will need to be in the office for in-person collaboration 3 times per quarter and therefore needs to be in a commutable distance from one of the following offices: San Francisco, Palo Alto, Seattle.\n \n #LI-SM4\n At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.\n Information regarding the culture at Pinterest and benefits available for this position can be found here . \n US based applicants only\n $222,716 — $389,753 USD \n Our Commitment to Inclusion: \n Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientati","salary_min":222716,"salary_max":389753,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["llm","fine-tuning","code-generation","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=7494765","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T21:22:51Z","expires_at":"2026-06-29T14:08:28.340035Z","created_at":"2026-05-27T14:08:42.016779Z","updated_at":"2026-05-30T14:08:28.454973Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/31539a5e-cc10-4277-ac39-bd766925c679"},{"id":"bc38cbd7-6147-49eb-a610-64fb031af669","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Staff Machine Learning Engineer, Voice AI ","slug":"staff-machine-learning-engineer-voice-ai-049973bf","description":"About the Role \n Together AI is building the best inference infrastructure for voice applications. Our Voice AI platform powers production-grade, real-time voice agents and applications — serving speech-to-text and text-to-speech models with best-in-class latency and reliability.\n We're looking for a Staff ML Engineer to drive the model serving layer for voice workloads. You'll work hands-on with inference engines like TRT-LLM and SGLang to optimize how we serve models like Whisper, Parakeet, Orpheus, and Kokoro — pushing latency and throughput to the frontier. You'll profile GPU utilization, design batching strategies for streaming audio, and ensure new model architectures can go from research to production quickly.\n This is a foundational hire on a small, high-impact team. Voice inference has unique challenges — streaming audio, tokenization, real-time latency budgets — that require dedicated ML engineering focus. You'll shape how Together serves voice models as the industry moves from pipeline architectures (ASR → LLM → TTS) toward end-to-end speech-to-speech.\n \n Own the model serving stack that powers Together's voice platform across STT, TTS, and speech-to-speech.\n Work directly with state-of-the-art accelerators (H100s, H200s, B200s) to optimize voice model inference.\n Collaborate with model partners (Cartesia, Deepgram, Rime, and others) to bring their models to production on Together's infrastructure.\n Build quality evaluation frameworks that guide model selection for customers and inform the roadmap.\n Join a small, early-stage team with outsized impact on a fast-growing product area.\n \n  \n Responsibilities \n \n Own the voice inference roadmap end-to-end — define and execute the technical strategy for optimizing STT, TTS, and speech-to-speech models across Together's infrastructure, with a clear-eyed view of where the field is heading and how to position the platform ahead of it.\n Drive best-in-class inference performance — architect and implement systems targeting leading TTFB, throughput, and GPU utilization for voice workloads; set the performance bar others in the industry measure against, not just catch up to.\n Lead productionization of voice models at scale — design the serving architecture for serverless and dedicated endpoints, including batching strategies, streaming inference pipelines, and memory management tailored to real-time audio; own reliability and latency SLAs.\n Build the voice evaluation platform — design a rigorous, extensible evaluation framework covering WER across accents, languages, and noise conditions for STT; naturalness, latency, and pronunciation fidelity for TTS; establish the internal benchmark methodology that informs model selection and roadmap decisions.\n Shape the architecture for next-generation model support — anticipate and enable emerging model paradigms — audio-native LLMs, codec-based architectures (SNAC, Encodec), and end-to-end speech-to-speech systems — before they're mainstream, not after.\n Serve as the technical DRI for model partner integrations — lead deep collaboration with partners such as Cartesia, Deepgram, and Rime; own the full lifecycle from integration to optimization to ongoing performance accountability.\n Diagnose and resolve the hardest performance problems in the stack — conduct systematic profiling and root-cause analysis from GPU kernel behavior to framework-level bottlenecks; drive shipped improvements with documented, measurable impact.\n Influence platform architecture across the organization — partner with platform engineering leadership to ensure the serving layer is built for the latency and reliability demands of real-time voice APIs; your technical decisions should raise the ceiling for the whole team.\n Define and scale voice fine-tuning capabilities — lead the technical direction for enabling customers to fine-tune STT and TTS models on Together's infrastructure, establishing the primitives for differentiated voice experiences.\n Lay technical foundations for a category-defining product surface — architect systems with enough foresight that they support multiple new voice products with minimal rework; think in terms of platforms, not point solutions.\n \n Requirements \n \n 8+ years of ML engineering experience, with a demonstrated focus on model serving, inference optimization, or ML infrastructure at production scale — including systems you've owned from design through live traffic.\n Deep, practical expertise in LLM serving engines (vLLM, SGLang, TensorRT-LLM, or equivalent) — you've modified engine internals, debugged edge cases under load, and contributed improvements back; you don't stop at the API surface.\n Expert-level Python and PyTorch proficiency, with a strong command of GPU optimization — CUDA kernels, memory hierarchies, profiling toolchains — and a track record of turning that knowledge into shipped latency or throughput wins.\n Proven system design judgment — you've made arch","salary_min":220000,"salary_max":280000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["mlops","gpu","llm","pytorch","fine-tuning","speech","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5140763007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T18:19:46Z","expires_at":"2026-06-29T14:01:50.400776Z","created_at":"2026-05-27T14:02:00.695384Z","updated_at":"2026-05-30T14:01:50.521421Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bc38cbd7-6147-49eb-a610-64fb031af669"}],"page":1,"per_page":20,"total":641,"total_pages":33}