{"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":"f8a29ca5-91b9-4ad7-9236-f57043203a4b","company_id":"ff51c80a-dce9-4cb4-b2e6-9c060d25ef55","title":"Robotics Engineer, Technical Lead","slug":"robotics-engineer-technical-lead-f0a6b32f","description":"About Applied Intuition\n Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co .\n We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. \n About the role\n This is a founding technical role helping build and shape Applied Intuition’s robotics organization from the ground up. You will set the technical direction for robotics software and AI, write production code, and build functional demos on physical hardware from day one. The role is weighted heavily toward software and learned behaviors, with the expectation that you can engage meaningfully across the hardware stack when needed.\n At Applied Intuition, you will:\n \n Define and own the technical architecture for humanoid robotics software, spanning perception, planning, control, and learned behaviors\n Write production-quality code in Python and C++ and ship it to physical robots — this is a hands-on individual contributor role first\n Design, train, evaluate, and deploy learning-based policies for manipulation and locomotion\n Build functional demonstrations on multiple robot hardware platforms that prove out capabilities and inform the product roadmap\n Establish simulation infrastructure and validate behaviors in physics-based environments before deploying to hardware\n Instrument robots, analyze telemetry and failure data, and iterate quickly to improve robustness in real-world conditions\n Work with teleoperation and data collection pipelines to generate training data and close the sim-to-real gap\n Identify and recruit the next engineers on the team\n \n We're looking for someone who has:\n \n BS, MS, or PhD in Robotics, Computer Science, Electrical Engineering, or a related field, or equivalent hands-on experience\n 7+ years of experience in robotics software development, with a meaningful portion on physical humanoid, legged, or highly dexterous manipulation platforms\n Proven track record shipping learning-based systems — behavior cloning, RL, or VLA policies — to real robots in production or near-production settings\n Strong proficiency in Python and C++ for robotics and ML systems; experience with PyTorch or equivalent deep learning frameworks\n Deep understanding of robotics fundamentals: kinematics, dynamics, control theory, state estimation, and perception\n Experience building and evaluating visuomotor or multimodal policies end to end, from data collection through deployment\n Ability to operate independently in an early-stage environment, make architectural decisions with limited information, and build from scratch\n Comfort on the lab floor — debugging physical robots, running hardware-in-the-loop tests, and iterating on live systems\n \n Nice to have:\n \n Experience with state-of-the-art bi-dexterous mobile hardware platforms, including dexterous manipulation and whole body control\n Familiarity with hardware bring-up, sensor integration, or embedded systems; ability to engage with mechanical and electrical teams at a subsystem level\n Background in SLAM, 3D perception, or sensor fusion (IMU, lidar, cameras, force/torque)\n Experience with physics simulators such as MuJoCo, NVIDIA Isaac Sim, or Gazebo\n Familiarity with ROS/ROS2 or similar robotics middleware\n Publication record or open-source contributions in robot learning, embodied AI, or manipulation\n \n Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment.\n Applied Intuition pay ranges reflect the ","salary_min":250000,"salary_max":400000,"location":"Sunnyvale, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["cloud","robotics","deep-learning","pytorch"],"apply_url":"https://boards.greenhouse.io/appliedintuition/jobs/4700425005?gh_jid=4700425005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T17:33:51Z","expires_at":"2026-06-29T14:03:36.517513Z","created_at":"2026-05-30T14:03:36.628511Z","updated_at":"2026-05-30T14:03:36.628511Z","company_name":"Applied Intuition","company_slug":"applied-intuition","company_logo_url":"https://www.google.com/s2/favicons?domain=appliedintuition.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f8a29ca5-91b9-4ad7-9236-f57043203a4b"},{"id":"530f705a-007a-497f-9f62-9a6e196ea9ad","company_id":"1df860e2-0800-48ea-81af-7121965be17a","title":"Engineering Manager - Machine Learning","slug":"engineering-manager-machine-learning-e1742de5","description":"Your work will change lives. Including your own. \n \n The Impact You’ll Make \n You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.\n In This Role You Will: \n \n Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.\n Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.\n Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards\n \n The Team You’ll Join: \n You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.\n The Experience You Will Need: \n \n Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.\n A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.\n Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture\n Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights \u0026 Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.\n Fluency in life sciences or drug discovery is a plus but not required to be considered.\n \n Working Location \u0026 Compensation: \n This is a remote position based in Toronto, Canada. \n At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is $210,070 to $282,851 (CAD) . You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. \n #LI-EP1\n The Values We Hope You Share: \n \n We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust. \n We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.\n We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.\n We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.\n We take ownership and accountability. Through ownership and accountability, we enable trust and autonomy—leaders take accountability for decisive action, and teams own outcomes ","salary_min":210070,"salary_max":282851,"location":"Toronto, Canada","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","agents","mlops","gpu","healthcare","deep-learning","pytorch","llm"],"apply_url":"https://job-boards.greenhouse.io/recursionpharmaceuticals/jobs/7961536","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T14:56:14Z","expires_at":"2026-06-29T14:07:04.607932Z","created_at":"2026-05-30T14:07:04.722791Z","updated_at":"2026-05-30T14:07:04.722791Z","company_name":"Recursion","company_slug":"recursion","company_logo_url":"https://www.google.com/s2/favicons?domain=recursion.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/530f705a-007a-497f-9f62-9a6e196ea9ad"},{"id":"58a5e82c-4cbd-49e1-9ce2-45a7b213b0a9","company_id":"1df860e2-0800-48ea-81af-7121965be17a","title":"Engineering Manager - Machine Learning","slug":"engineering-manager-machine-learning-288c8ba8","description":"Your work will change lives. Including your own. \n \n The Impact You’ll Make \n You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.\n In This Role You Will: \n \n Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.\n Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.\n Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards\n \n The Team You’ll Join: \n You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.\n The Experience You Will Need: \n \n Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.\n A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.\n Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture\n Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights \u0026 Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.\n Fluency in life sciences or drug discovery is a plus but not required to be considered.\n \n Working Location \u0026 Compensation: \n This is an office-based, hybrid position at our US headquarters located in Salt Lake City, Utah . Employees are expected to work in the office at least 50% of the time.\n At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is $151,130 to $203,490 (USD) . You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. \n #LI-EP1\n The Values We Hope You Share: \n \n We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust. \n We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.\n We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.\n We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.\n We take ownership and accountability. Through ownership and acco","salary_min":151130,"salary_max":203490,"location":"Salt Lake City, Utah","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["pytorch","deep-learning","cloud","mlops","gpu","llm","distributed-systems","healthcare"],"apply_url":"https://job-boards.greenhouse.io/recursionpharmaceuticals/jobs/7961460","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T14:56:13Z","expires_at":"2026-06-29T14:07:04.532978Z","created_at":"2026-05-30T14:07:04.642889Z","updated_at":"2026-05-30T14:07:04.642889Z","company_name":"Recursion","company_slug":"recursion","company_logo_url":"https://www.google.com/s2/favicons?domain=recursion.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/58a5e82c-4cbd-49e1-9ce2-45a7b213b0a9"},{"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":"64170ac3-3bc0-4e64-aa55-14d395814525","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Machine Learning Engineer II, Ads Response Prediction","slug":"senior-machine-learning-engineer-ii-ads-response-prediction-c8a2de33","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n As a Senior Machine Learning Engineer II on the Ads Response Prediction team, you will lead the design and development of core ML models that power Instacart’s ads ecosystem. This is a research-leaning role focused on theoretical problem formulation, training methodology, and model quality rather than infrastructure or full-stack engineering. You will tackle fundamental challenges in pCTR modeling such as mitigating selection bias, position bias, and optimizer’s curse in training data, improving model calibration across surfaces and domains, and advancing our multi-task learning and sequence modeling capabilities. You will also have the opportunity to shape our next-generation foundation model approach for ads ranking and contribute to cutting-edge retrieval systems like TIGER (Transformer Index for Generative Recommenders), Semantic ID and domain language models.\n The Ads Response Prediction team owns all systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart. This includes search and exploration retrieval systems, sequential modeling and generative retrieval systems for next interaction recommendations, LLM integrations, relevance models, pCTR models, bidding models and incrementality models. The team optimizes for an efficient marketplace to ensure delightful customer shopping experience, desirable advertiser business outcome and Instacart Ads revenue.\n The team has strong ML infrastructure and MLOps support, including Delta/DBT-Spark data pipelines, Ray-based distributed training, and automated model deployment. This means you can focus your energy on advancing modeling science rather than building infrastructure.\n About the Job \n \n Lead research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases (selection bias, position bias, optimizer’s curse), and advancing model accuracy across Instacart’s ads surfaces.\n Design and implement debiasing techniques such as Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression) to address systematic prediction biases.\n Contribute to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts (MoE), Transformer layers for sequential user behavior, and LoRA adaptors for scalable domain fine-tuning.\n Drive sequence modeling initiatives including the TIGER generative retrieval system and Semantic ID representation learning, expanding their application across ads surfaces such as Product Details, Search and other placements.\n Collaborate with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction.\n Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., overcalibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.\n Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.\n \n About You \n Minimum Qualifications \n \n PhD/Master in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.\n 6+ years of combined academic and industry experience (including PhD research) applying ML to ranking, recommendation, or prediction problems at scale.\n Deep understanding of CTR/conversion prediction modeling, including familiarity with architectures such as Deep \u0026 Wide, DeepFM, DCN, and multi-task learning formulations.\n Strong foundation in causal inference, counterfactual reasoning, and","salary_min":201000,"salary_max":212000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["llm","pytorch","deep-learning","generative-ai","data-pipeline","mlops","fine-tuning","distributed-systems"],"apply_url":"https://instacart.careers/job/?gh_jid=7963838","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T19:10:27Z","expires_at":"2026-06-29T14:08:41.426586Z","created_at":"2026-05-29T14:32:35.186075Z","updated_at":"2026-05-30T14:08:41.541147Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/64170ac3-3bc0-4e64-aa55-14d395814525"},{"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":"88b5244c-2383-4f06-b5ef-0ade11296098","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Technical Lead Manager, Prediction \u0026 Planning, Machine Learning Eval","slug":"staff-technical-lead-manager-prediction-planning-ml-eval-29b43259","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver’s behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently. \n We have an exciting opportunity for a Staff Technical Lead Manager to lead our ML Evaluation team. In this role, you will define the strategic vision for our evaluation platforms, scaling the critical infrastructure and metrics required, and partner closely with the modeling teams to rigorously validate our next-generation deep neural networks and accelerate ML developer velocity across PrePlan.\n You will: \n \n Influence the strategic direction of foundational infrastructure and evaluation platforms to robustly support next-generation ML model evaluation use cases\n Collaborate cross-functionally with ML engineers, data scientists, and infrastructure teams to identify, define, and surface critical signals on model, component, and system-level performance\n Leverage and scale evaluation and infrastructure platforms to significantly enhance the ML developer experience, enabling faster iteration through earlier, more reliable, and trusted model evaluation\n Manage and mentor a focused team of engineers, aligning their career growth and aspirations with critical organizational needs\n Drive best practices and leverage deep technical awareness of the Alphabet ML stack (e.g., TensorFlow, JAX, Flax, Apache Beam) to optimize evaluation workflows\n Stay at the forefront of emerging technologies, industry trends, and research in ML evaluation methodologies and advanced metrics design\n \n You have:  \n \n M.S. in Computer Science, Mathematics, or equivalent industry experience in Robotics or large-scale ML systems with critical evaluation needs\n 5+ years of experience building and maintaining large-scale distributed infrastructure, ML inference systems, or evaluation platforms, including 3+ years of engineering management experience\n Strong coding and testing proficiency, specifically in Python and C++\n Strong foundational knowledge of model evaluation and core data science principles (e.g., confidence intervals, outlier identification, curve fitting, and causality analysis)\n Familiarity with large-scale ML deployment and orchestration tools (e.g., TF Serving, TorchServe, Kubeflow, SageMaker Pipelines, or Vertex AI Pipelines)\n Understanding of machine learning fundamentals and experience with popular ML frameworks such as JAX, PyTorch, or TensorFlow\n \n We prefer: \n \n Experience developing and maintaining evaluation pipelines for ML models\n Experience deploying and supporting machine learning models for computer vision, natural language processing, robotics/motion planning, or recommendation systems\n Experience supporting a small team of MLEs developing high-capacity, production-grade models and components\n Strong understanding of metrics computation and regression detection at scale\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["robotics","nlp","computer-vision","pytorch","autonomous-vehicles","deep-learning","tensorflow","evaluation"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7963516","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T17:26:50Z","expires_at":"2026-06-29T14:04:32.30248Z","created_at":"2026-05-29T14:12:24.077985Z","updated_at":"2026-05-30T14:04:32.417838Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/88b5244c-2383-4f06-b5ef-0ade11296098"},{"id":"0ed6f2c3-8d05-4541-a58a-3bc3eb48b078","company_id":"1a3abe34-d1c1-45b9-9259-3e2e007a961c","title":"Staff Research Scientist","slug":"staff-research-scientist-6193df9d","description":"About Voyage AI Team at MongoDB\n Voyage AI team in MongoDB is building a best-in-class, general-purpose, domain-specific, and fine-tuned embedding models and rerankers to enable accurate, efficient unstructured data search and retrieval for RAG, recommendation, semantic search, and more. It is backed by a strong team of AI researchers from Stanford, MIT, Berkeley, Princeton, and CMU, who have conducted over five years of cutting-edge research on training embedding models. Voyage AI was acquired by MongoDB recently, and is now integrating the SOTA embedding models with MongoDB's data platform to create powerful end-to-end solutions.\n Position Overview\n We are seeking a Staff Research Scientist to join our team and contribute to the development of next-generation AI models. This position offers a unique opportunity to work on challenging problems at the intersection of machine learning research and practical deployment of large neural networks.\n This role can be based out of our Palo Alto office, or remotely in the United States.\n Responsibilities\n \n Conduct cutting-edge research in artificial intelligence, from frontier LLMs to embedding models and rerankers\n Innovate in next-generation information retrieval and LLM agent paradigm\n Collaborate closely with other research scientists and research engineers as well as peers across the organization\n \n Qualifications\n \n PhD degree in Computer Science or related field\n A track record of coming up with new ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications in top venues\n Strong background in machine learning, deep learning, and natural language processing\n Experience building complex neural networks for language and visual understanding\n Capable of conducting rigorous empirical studies to validate theoretical results\n Excellent leadership, problem-solving, and communication skills\n \n What We Offer\n \n Opportunity to work on real-world problems at the cutting edge of AI research\n Opportunity to utilize research vision to innovate the entire company and make real-world impact\n Exposure to the full lifecycle of AI model development, from research to production\n Our compensation (base + equity) for this position is competitive with frontier AI labs\n \n About MongoDB \n MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform—the most widely available, globally distributed database on the market—helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available across AWS, Google Cloud, and Microsoft Azure.\n With offices worldwide and nearly 60,000 customers—including 75% of the Fortune 100 and AI-native startups—relying on MongoDB for their most important applications, we’re powering the next era of software.\n Our compass at MongoDB is our  Leadership Commitment,  guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB.\n To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone.  From employee affinity groups, to fertility assistance and a generous parental leave policy , we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys.  Learn more about what it’s like to work at MongoDB , and help us make an impact on the world!\n MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.\n MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions 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.\n Req ID: 2273454547\n MongoDB’s base salary range for this role is posted below. Compensation at the time of offer is unique to each candidate and based on a variety of factors such as skill set, experience, qualifications, and work location. Salary is one part of MongoDB’s total compensation and benefits package. Other benefits for eligible employees may include: equity, participation in the employee stock purchase program, flexible paid time off, 20 weeks fully-paid gender-neutral parental leave, fertility and adoption assistance, 401(k) plan, mental health counseling, access to ","salary_min":151000,"salary_max":297000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["nlp","computer-vision","search","llm","embeddings","deep-learning","research"],"apply_url":"https://www.mongodb.com/careers/job/?gh_jid=7956670","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T17:21:23Z","expires_at":"2026-06-29T14:08:48.853182Z","created_at":"2026-05-29T14:32:41.960202Z","updated_at":"2026-05-30T14:08:48.964003Z","company_name":"MongoDB","company_slug":"mongodb","company_logo_url":"https://www.google.com/s2/favicons?domain=www.mongodb.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0ed6f2c3-8d05-4541-a58a-3bc3eb48b078"},{"id":"6dc81f39-064c-435f-95c1-b6c70be6a1c5","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Machine Learning Engineer, App SW","slug":"machine-learning-engineer-app-sw-73eaf56f","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 As an  ML Engineer  within the Application Engineering team, you’ll lead critical initiatives that push the frontier of model-based autonomous driving—both in terms of core driving performance and feature-level intelligence such as personalisation, comfort, and collaboration.\n You’ll design and deliver ML-driven behaviors that scale from assisted to autonomous driving. Your work will span across model architecture, data pipelines, evaluation frameworks, and real-world deployment. You’ll collaborate deeply with AI Platform, Simulation, Robot SW and Model Release teams to build systems that are performant, adaptable, and ready for production.\n Responsibilities:\n \n Develop and improve end-to-end driving models with state-of-the-art performance, robustness, and generalization.\n Lead projects on personalized and collaborative driving, including behavior conditioning, comfort tuning, and user alignment.\n Build evaluation pipelines and metrics for both closed-loop and open-loop driving performance and product readiness.\n Curate and mine real-world and synthetic data to drive scenario diversity, coverage, and feature-specific development.\n Influence architecture choices, training methodologies, and deployment pathways for production-scale learning systems.\n Collaborate cross-functionally across various teams to ensure integration and iteration velocity.\n Mentor senior engineers and shape the long-term technical direction across Autonomy.\n \n About you:  \n In order to set you up for success as a Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.  \n Essential\n \n Extensive and proven track record of shipping deep learning systems to production.\n Expert in deep learning (esp. sequential models, control, planning, or perception).\n Proficient in Python and other relevant languages (e.g. C++ and CUDA) and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices.\n Experience with real-time systems or robotics, ideally with simulation- or vehicle-in-the-loop components.\n Ability to lead technical initiatives across teams, drive alignment, and mentor engineers.\n \n Desirable\n \n Prior work in autonomous driving, imitation learning, or trajectory prediction.\n Familiarity with personalization, human behavior modeling, or driver intent inference.\n Experience integrating ML systems into production hardware or multi-agent simulation.\n \n This role is a full-time role based in Sunnyvale or Detroit (hybrid) and the reasonably estimated salary for this role ranges from $283,500 to $381,600, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.\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 creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. \n We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition  (including breastfeeding) or any other ba","salary_min":283500,"salary_max":381600,"location":"Detroit","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["autonomous-vehicles","data-pipeline","robotics","pytorch","generative-ai","deep-learning","gpu","agents"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8568694002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T14:51:17Z","expires_at":"2026-06-29T14:12:44.75073Z","created_at":"2026-05-29T14:50:38.39389Z","updated_at":"2026-05-30T14:12:44.866872Z","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/6dc81f39-064c-435f-95c1-b6c70be6a1c5"},{"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":"dc3be083-3d88-4196-ae36-689fe5f1b3fe","company_id":"654d4532-88db-435d-8a6f-161b8c5a491e","title":"Senior Manager, Data Science - Foundational Models","slug":"senior-manager-data-science-ee646aa7","description":"About Stitch Fix, Inc. \n Stitch Fix (NASDAQ: SFIX) Stitch Fix is redefining retail by combining human creativity with advanced data science and Generative AI. As we build the future of personalized shopping, we’re equally committed to building yours.  We believe in investing in our team as much as our technology. Join us to be a trendsetter in the industry and help us redefine what’s possible for our clients, while we help you reach your full potential.\n  \n About the Role \n At Stitch Fix, we are at the forefront of innovation, creating cutting-edge solutions that blend fashion, technology, and data science. Our data science team combines machine learning with expert human judgment to generate innovative recommendations and insights that transform the way our clients discover what they love. We believe in a curiosity-driven data science culture where members are empowered to deliver impact through end-to-end model development. The diversity of the problems that we work on and the data-rich environment of our business make it possible, even essential, to bring the tools of multiple disciplines to bear on our hardest problems.  \n We are looking for an experienced Foundational Models Team Manager to lead a group of talented machine learning engineers and data scientists. In this role, you will shape the future of fashion technology by driving the development and deployment of our core scoring and ranking algorithms. These industry-leading machine learning models match clients to available inventory, supporting our stylists in designing Fixes and powering online personalization directly to our clients.\n Responsibilities: \n \n Champion cutting-edge machine learning and AI techniques to improve holistic client engagement, personalization, and overall growth.\n Represent our core algorithmic capabilities in cross-functional forums, including with our executive leadership team, synthesizing business requirements and translating technical solutions with radical transparency and a solution-oriented mindset.\n Lead and inspire a team building transformative capabilities at the heart of our company’s value proposition, synthesizing and balancing multiple business objectives.\n Foster a culture of ownership for holistic business outcomes within the team, encouraging proactive engagement with cross-functional partners.\n Drive the development and optimization of our prediction and recommendation algorithms, ensuring they provide personalized, relevant, and engaging fashion recommendations for stylists and clients.\n Oversee the end-to-end algorithm development lifecycle—from ideation and experimentation to testing and deployment in a production environment.\n Manage the prioritization and execution of key algorithmic projects while balancing business needs, technical feasibility, and timelines.\n Implement industry best practices for team collaboration, code quality, use of AI, and data management.\n Stay up-to-date with the latest trends and advancements in AI-assisted development, AI-enabled product experiences, machine learning, and fashion technology.\n \n About You \n This is what you’ll need to succeed in this role from day 1. \n Requirements: \n \n Bachelor’s Degree in a quantitative field such as Computer Science, Statistics, Physics, Mathematics, or a related field required. Master’s or PhD preferred. \n 5+ years of experience in design and deployment of machine learning algorithms, ideally in retail applications of deep learning and recommendation systems.\n 2+ years of experience in a direct people management role, with a track record of inspiring and motivating direct reports, and connecting team priorities to business objectives.\n Ability to write and review production-grade code, ideally in Python.\n Applied knowledge of AI-assisted coding best practices and development of agentic product solutions.\n Excels at building trust with your team, stakeholders, and technical partners.\n Excellent communication skills with the ability to articulate complex technical concepts to non-technical audiences.\n Experience with online A/B testing, experimentation frameworks, and performance metrics.\n Familiar with cloud-based infrastructure and distributed data systems.\n Compensation and Benefits This role will receive a competitive salary, benefits, and equity. The salary for US-based employees hired into this role will be aligned with the range below, which includes our three geographic areas. A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, location, and performance. This position is eligible for an annual bonus, and new hire and ongoing grants of restricted stock units, depending on employee and company performance. In addition, the position is eligible for medical, dental, vision, and other benefits. Applicants should apply via our internal or external careers site. \n Salary Range\n $200,000 — $246,000 USD \n This link leads t","salary_min":200000,"salary_max":246000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["deep-learning","healthcare","agents","generative-ai","payments","data-science"],"apply_url":"https://www.stitchfix.com/careers/jobs?gh_jid=7947680\u0026gh_jid=7947680","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T19:21:44Z","expires_at":"2026-06-29T14:18:30.789303Z","created_at":"2026-05-28T14:20:06.359409Z","updated_at":"2026-05-30T14:18:30.909247Z","company_name":"Stitch Fix","company_slug":"stitch-fix","company_logo_url":"https://www.google.com/s2/favicons?domain=stitchfix.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/dc3be083-3d88-4196-ae36-689fe5f1b3fe"},{"id":"a148f68f-899f-448a-a1c1-dcb27af55e25","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Safety System Engineer","slug":"safety-system-engineer-c6b6d960","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 Your Responsibilities\n In collaboration with the other safety expert(s):\n \n Lead the functional safety activities for Wayve’s ADS, and its integration into present and future vehicle platforms, or the integration of third party safety-critical systems. This involves:\n \n Working with various internal and external teams to gain a deep understanding of the system under development to build the Item Definition work product for each platform.\n Conduct risk assessments (HARAs, FMEAs, FTAs) to identify the risks and build a safety concept to mitigate the risks to an acceptable level.\n Work with internal design teams to build a system architecture that meets the safety concept including decisions on decompositions, deriving relevant SW and external requirements.\n Support internal SW teams in implementing the safety requirements on target HW.\n Define test specifications at various levels of integration to verify and finally validate the safety concept.\n Define the Safety case by building a structured claims, arguments and evidence based paradigm.\n \n Contribute to developing the safety work products for production projects with similar activities as defined above.\n Work with the process and compliance teams to update the safety processes for certification activities.\n \n Your Skills\n Mandatory: \n \n Very good understanding of the functional safety development process as per ISO 26262 with a minimum 5 years of experience in applying the standard on different automotive systems; in particular parts 3, 4, 5, 6, 8 and 9.\n Experience in conducting vehicle level testing activities at proving grounds to verify and validate the safety concept.\n \n Desirable \n \n Expertise in ISO 26262-6; the candidate must have practical experience in developing or leading the development of ISO 26262 compliant production software and in the qualification of software tools.\n Experience in managing suppliers of safety related hardware or software elements (DIA design, supplier audit and assessment, project management)).\n Knowledge and experience in developing the safety case of an AD systems, in particular:\n \n Experience in developing the safety concept for a computer vision based system\n Sufficient knowledge of neural networks (architectures, algorithms, training and testing pipeline) to understand the challenges associated with the safety assurance of such systems and discuss options and rationales with our scientists\n \n Experience in SOTIF, especially in identifying SOTIF triggering conditions through analysis and defining testing and validation strategies to assess SOTIF performance.\n Operational safety experience, such as identifying the operational risks and designing training and operational procedures to ensure the safety of testing on public roads, is a plus.\n \n About yourself\n \n You are truly passionate about Safety and making the world a safer place your mission; this is reflected in the way you choose terms appropriate to your audience, present the essence of system safety and formulate the key takeaway messages.\n You are pragmatic, you understand the need to balance risk versus benefits, even more so in a startup environment.\n You are comfortable in debating with other stakeholders safety-related trade-offs, at any level of the development process and at any level of the organisation.\n You are self-starter, autonomous in thoughts but collective in action.\n You are able to think and work at various abstraction levels; thinking “blue sky” and paving the way for long term safety goals, or diving deep into technical details to provide clear guidance and solutions on the safety activities for the development of a subsystem (e.g. controller) or launching an application (robotaxi)\n You are confident in challenging others’ ideas and views, but equally, are open to constructive criticism and able to reconsider your opinions or beliefs.\n \n This role is a full-time role based in Sunnyvale, CA (hybrid) and the reasonably estimated sala","salary_min":209700,"salary_max":298200,"location":"Sunnyvale, CA","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["computer-vision","deep-learning","autonomous-vehicles","generative-ai"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8564863002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-26T18:37:28Z","expires_at":"2026-06-29T14:12:46.822059Z","created_at":"2026-05-27T14:13:10.98323Z","updated_at":"2026-05-30T14:12:46.935289Z","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/a148f68f-899f-448a-a1c1-dcb27af55e25"},{"id":"30691bcd-dc37-4149-9f33-16fd4c446705","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Senior Data Scientist, Guest Travel Insurance (Algorithms)","slug":"senior-data-scientist-guest-travel-insurance-algorithms-d5dcd08e","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 The Community You Will Join: \n Airbnb is a mission-driven company dedicated to helping create a world where anyone can belong anywhere. Travel should feel safe—and AirCover is how we deliver on that promise. Through Guest Travel Insurance (GTI), we offer guests peace of mind at the moment of booking and throughout their trip. As a Data Scientist on AirCover, you’ll work at the intersection of insurance, personalization, and machine learning—building intelligent systems that help the right guest discover the right coverage at the right moment. You’ll join a tight-knit, high-output DS team that runs one of Airbnb’s most experiment-dense personalization roadmaps, partnering daily with product, engineering, operations, and legal to ship work that directly affects guest trust and revenue.\n The Difference You Will Make: \n We’re looking for a machine learning expert who is excited to own hard problems end-to-end—from prototype to production. You’ll have direct scope to contribute and lead across:\n \n Package personalization \u0026 ML-based recommendation: Evolve rule-based guest segmentation into a full ML recommendation system that surfaces the right insurance (e.g., trip cancellation, accidental damage coverage, on-trip protection) to each guest based on purchase intent, trip attributes, listing signals, and user history.\n Content personalization: Build models that rank and select benefit messaging for each guest—deciding which coverages to highlight, in what order, and with what framing—drawing on learnings from segmentation experiments and LLM-assisted content prototyping.\n Intent modeling: Develop and productionize ML models (from gradient-boosted trees to deep learning) that predict a guest’s likelihood to value specific coverages, using structured booking data and unstructured signals.\n Journey understanding and optimization: Leverage reinforcement learning to personalize across user journey, with understanding on user preferences on entry point, price, notification frequency, and trip characteristics\n High-velocity experimentation: Design and run adaptive experiments to maximize learning within tight traffic constraints; sequence ERFs strategically to keep the personalization roadmap moving.\n \n A Typical Day: \n \n Dig into experiment results to surface high-impact personalization opportunities; translate what you find into crisp scientific problem formulations that balance rigor with speed-to-learning.\n Work closely with product managers, engineers, operations, legal, and privacy partners to align on ML requirements, de-risk design decisions, and gather requirements on explainability and compliance.\n Hands-on develop, evaluate, and ship ML models and data pipelines at scale—batch and real-time, structured and unstructured—using Airbnb’s paved-path tooling and AI native mindset\n Prototype and iterate quickly: turn a new idea into a working model in a prototype, get early signals from an experiment, then productionize what works. You move fast and don’t wait to be asked.\n Present findings and proposals at team reviews and to technical, product, and executive stakeholders—making complex ML results legible without dumbing them down, and generating conviction on the roadmap ahead.\n Stay current with the research community; draw on state-of-the-art advances in recommendation systems, LLMs, and personalization to raise the bar for what the team ships. Occasionally publish externally or present at conferences to advance Airbnb’s scientific standing.\n \n Your Expertise: \n \n 5+ years of relevant industry experience (e.g., ML scientist, tech lead, junior faculty) and a Master’s degree or PhD with 2+ yrs in a relevant field.\n Proven hands-on experience building and shipping personalization and recommendation systems at scale: strong intuition for feature engineering, user modeling, and the full ML lifecycle (training, serving, monitoring, iteration). Experience with LLMs, Computer Vision or content-understanding topics is a strong plus.\n Strong fluency in Python and SQL; hands-on experience with TensorFlow or PyTorch, Airflow, and a data warehouse environment.\n Deep understanding of ML algorithms (gradient-boosted trees, deep learning, optimization) and experiment design—including A/B testing, multi-armed bandits, and the practical constraints of running experiments at scale. Causal inference skills are a plus.\n Exceptional communicator: you can make complex ML work legible to engineers, product managers, legal, and executives alike— written and verbal. You treat communication as a core part of the job, not an afterthought.\n Self","salary_min":179000,"salary_max":210000,"location":"United States","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["tensorflow","llm","deep-learning","pytorch","data-pipeline","computer-vision","reinforcement-learning","data-science"],"apply_url":"https://careers.airbnb.com/positions/7926614?gh_jid=7926614","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-21T15:55:19Z","expires_at":"2026-06-29T14:09:02.248406Z","created_at":"2026-05-27T14:09:19.322462Z","updated_at":"2026-05-30T14:09:02.357735Z","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/30691bcd-dc37-4149-9f33-16fd4c446705"},{"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":"42f8830a-ebfa-421c-a5b0-188eb93d4cd8","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Applied Research Scientist, Multi-Modal Perception (PhD New Grad)","slug":"applied-research-scientist-multi-modal-perception-phd-new-grad-74815077","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the autonomously driving vehicle (ADV), i.e., the system that “perceives” the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware.\n In this hybrid role you will report to a Technical Lead Manager.\n You will: \n \n Own tasks in the ML Driver, take responsibility for task scaling and task performance, create ML methods and recipes to scale and improve tasks.\n Analyze behavior of ML systems in real-world application, identify issues and root causes, advise or develop short- and long-term solutions.\n Monitor ML systems in production, develop methods for automatically detecting issues or regressions, develop AI-aided analysis and debugging tooling.\n Develop and maintain metrics for ADV relevant issues, including safety-critical and longtail issues.\n \n You have: \n \n Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience\n 3+ years experience in Machine Learning and Computer Vision\n Experience with Python\n Experience with ML frameworks like PyTorch or JAX\n \n We prefer: \n \n MS or PhD Degree in Machine Learning, Robotics, Computer Science or a similar discipline\n Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, IROS, RSS, NeurIPS, AAAI, IJCV, PAMI\n Experience with C++\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $175,000 — $215,000 USD","salary_min":175000,"salary_max":215000,"location":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"mid","tags":["computer-vision","autonomous-vehicles","pytorch","deep-learning","robotics","research"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7948348","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-20T23:48:38Z","expires_at":"2026-06-29T14:04:23.835364Z","created_at":"2026-05-27T14:04:33.548406Z","updated_at":"2026-05-30T14:04:23.954594Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/42f8830a-ebfa-421c-a5b0-188eb93d4cd8"},{"id":"8068616e-2c82-4290-9a16-ebd2c6fcd45b","company_id":"332b7698-676b-4a3e-8b02-81b1195c5af6","title":"Sr. Staff AI Research TLM - AI Systems","slug":"sr-staff-ai-research-tlm-ai-systems-9edbec07","description":"Principal Research Scientist – Scaling\n P-1227\n About Databricks AI\n At Databricks, we are obsessed with enabling data teams to solve the world’s toughest problems, from security threat detection to cancer drug development, by building and running the world’s best data and AI platform. The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI is committed to the belief that a company’s AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all.\n About the Scaling Research Team\n The Databricks AI Scaling team focuses on pushing the boundaries of large language model (LLM) training and inference efficiency beyond what is required to support existing models. The team explores novel avenues for scaling and efficiency improvements across algorithms, systems, and infrastructure, requiring researchers who can both drive independent research agendas and dive deep into low‑level implementation details with engineering partners.\n Role Summary\n As a Principal Research Scientist – Scaling, you will lead a team of world‑class researchers and engineers to advance the state of the art in large‑scale machine learning, focusing on post-training, RL and inference efficiency, optimization, and scaling. You will define and execute a research roadmap that advances the Databricks AI platform and delivers tangible improvements to how customers train, serve, and adapt LLMs at scale, working closely with product, data, and engineering leaders to bring cutting‑edge methods into production.\n The Impact You Will Have\n \n Lead and grow a multidisciplinary research team focused on foundational and applied AI problems, with a particular emphasis on LLM scaling, efficiency, and systems performance.\n Define the scaling research roadmap in alignment with Databricks’ strategic objectives, prioritizing advances in foundation model efficiency and large‑scale training and inference.\n Drive algorithmic innovations for large‑scale neural network training and inference, including novel optimizers, low‑precision techniques, and model adaptation methods, and guide your team in rigorous empirical validation against state‑of‑the‑art approaches.\n Optimize end‑to‑end ML systems for distributed training and RL, memory efficiency, and compute efficiency through close collaboration with core systems and platform teams, ensuring that research ideas translate into performant, reliable infrastructure.\n Partner with product and engineering to translate research breakthroughs, especially around scaling and efficiency, into customer‑impacting capabilities in the Databricks AI platform.\n Foster a culture of scientific excellence and openness, including high‑quality research practices, reproducible experimentation, and effective internal knowledge sharing across Databricks AI.\n Represent Databricks AI research externally through top‑tier publications, conference talks, and collaborations with academia and the open‑source community, with a focus on optimization and efficiency for large‑scale models.\n Mentor and develop talent, providing both technical guidance (research agendas, experimentation, implementation) and career development support for research scientists and engineers.\n \n What You Will Do\n \n Define and lead independent research programs on   foundation model efficiency, covering topics such as optimizer design, low‑precision training/inference, scalable model architectures, and efficient adaptation methods.\n Oversee the design and execution of large‑scale experiments, including benchmarking against state‑of‑the‑art methods and evaluating trade‑offs in quality, latency, throughput, and cost.\n Work hands‑on with your team on high‑quality, efficient code in Python and PyTorch for research implementation, rapid prototyping, and integration with Databricks’ production systems.\n Collaborate with distributed systems and infra teams to push the limits of distributed training ,  parallelism strategies, memory management, and hardware utilization for LLMs and other large models.\n Establish metrics, evaluation protocols, and best practices for scaling‑focused research (e.g., training efficiency, inference cost, energy usage) and drive their adoption across Databricks AI.\n Champion responsible and robust deployment of scaling innovations, ensuring that model behavior, reliability, and safety remain first‑class considerations.\n \n What We Look For\n \n Proven ability to lead a research team to develop novel techniques for foundation model efficiency and related topics, with a strong track record of industry impact. \n Deep expertise in at least one of: generative AI, LLMs, distributed ML systems, model optimization, or responsible AI, with a strong emphasis on scaling a","salary_min":270000,"salary_max":340000,"location":"Mountain View, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["generative-ai","distributed-systems","llm","agents","pytorch","deep-learning","data-pipeline","research"],"apply_url":"https://databricks.com/company/careers/open-positions/job?gh_jid=8557780002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-20T09:40:26Z","expires_at":"2026-06-29T14:02:00.176089Z","created_at":"2026-05-27T14:02:12.463367Z","updated_at":"2026-05-30T14:02:00.284419Z","company_name":"Databricks","company_slug":"databricks","company_logo_url":"https://www.google.com/s2/favicons?domain=databricks.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8068616e-2c82-4290-9a16-ebd2c6fcd45b"},{"id":"a9c29f5c-7c91-4d7d-a1b5-b6b481d3c7dd","company_id":"5fac52d7-9b0b-4990-80a2-e2949dd0af1d","title":"Staff Engineer, ML/AI Platform","slug":"staff-engineer-mlai-platform-14255373","description":"Attentive® is the AI marketing platform for 1:1 personalization redefining the way brands and people connect. We’re the only marketing platform that combines powerful technology with human expertise to build authentic customer relationships. By unifying SMS, RCS, email, and push notifications, our AI-powered personalization engine delivers bespoke experiences that drive performance, revenue, and loyalty through real-time behavioral insights.\n  \n Recognized as the #1 provider in SMS Marketing by G2, Attentive partners with more than 8,000 customers across 70+ industries. Leading global brands like Crate and Barrel, Urban Outfitters, and Carter’s work with us to enable billions of interactions that power tens of billions in revenue for our customers.\n  \n With a distributed global workforce and employee hubs in New York City, San Francisco, London, and Sydney, Attentive’s team has been consistently recognized for its performance and culture. We’re proud to be included in  Deloitte’s Fast 500  (four years running!),  LinkedIn’s Top Startups ,  Forbes’ Cloud 100 (five years running!),  Inc.’s Best Workplaces , and the  Human Rights Campaign Foundation's Corporate Equality Index !\n About the Role We’re seeking an accomplished Staff Software Engineer to join Attentive’s Machine Learning Platform team as a high-impact individual contributor focused on building the AI and ML infrastructure that powers our AI product suite. You’ll architect and build the foundational platform components that enable AI / ML engineers and data scientists to train, deploy, and serve models and agentic infrastructure with velocity, performance, and reliability at scale. As a Staff-level IC, you’ll operate as a technical force multiplier, setting the technical direction for AI and ML infrastructure across Attentive’s AI organization. You’ll lead through influence and technical excellence, advocating for long-term architectural progress while balancing immediate platform needs. Your work will span strategic initiatives measured in quarters and years, focusing on high-leverage decisions that enable entire teams to ship AI and ML capabilities faster and more reliably. Strategic Need Attentive is revolutionizing the digital shopping experience across every channel through our AI product suite for half a billion subscribers. We’re looking for a high impact individual to take our platform from v1 to vNext and beyond — supporting the full spectrum of AI and ML workloads at massive scale. We support traditional models and deep learning today, and we are growing into reinforcement learning and agentic infrastructure quickly. This is a ground-floor opportunity to drive and influence the architectural roadmap for Attentive’s entire AI and ML ecosystem toward self-service workflows, real-time inference at scale, agentic capabilities, and robust model lifecycle management.\n What You’ll Accomplish \n \n Setting Technical Direction - Architect ML platform strategy spanning data pipelines, training infrastructure, and serving layers using cutting-edge tooling like Ray, MLFlow, Metaflow, Argo, and Spark.\n Uplevel and Innovate Core AI \u0026 ML Stack - Build and operate production-grade, low-latency ML serving layers with  robust model lifecycle systems including champion/challenger testing, automated rollouts, versioning, and rollback capabilities.\n Uplevel and Innovate Core AI \u0026 ML Stack - Define and drive Attentive’s agentic stack.\n Technical Leadership - Provide ML infrastructure perspective in high-level discussions about Attentive’s AI strategy spanning multiple quarters and teams.\n Technical Mentorship - Mentor platform and ML engineers, actively championing team members.\n Being the “Glue” - Build universal interfaces, architectures, and patterns—like data access layers and prediction serving APIs—that bridge platform capabilities with product needs to streamline high-priority ML work across the organization.\n \n Your Expertise \n \n You have the experience to know what works, what doesn’t, and why in AI and ML systems.\n 5+ years focused specifically on ML Platform/MLOps, with deep understanding of gold-standard practices and best-in-class tooling.\n Proven track record of owning and building core components of ML platforms using tools like Spark, Ray, MLFlow, Kubeflow, or Metaflow.\n You’ve built and operated a high-throughput agentic stack (MCP / data infrastructure, context store, orchestration, and prompt layer).\n Strong expertise in Python for both batch processing and online service frameworks.\n Experience designing and operating online and offline inference systems, understanding the critical differences and tradeoffs between them.\n \n Sample Projects \n \n Design and implement inference pipelines with champion/challenger shadow testing and automated model promotion.\n Lead and scale Attentive’s agentic stack from the ground up.\n Scale real-time feature streaming to handle low-latency, high-vo","salary_min":170000,"salary_max":280000,"location":"United States","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["deep-learning","agents","reinforcement-learning","mlops","data-pipeline","platform"],"apply_url":"https://job-boards.greenhouse.io/attentive/jobs/4251570009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-19T16:02:10Z","expires_at":"2026-06-29T14:18:28.312181Z","created_at":"2026-05-27T14:19:20.280182Z","updated_at":"2026-05-30T14:18:28.427456Z","company_name":"Attentive","company_slug":"attentive","company_logo_url":"https://www.google.com/s2/favicons?domain=attentive.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a9c29f5c-7c91-4d7d-a1b5-b6b481d3c7dd"},{"id":"657568b2-56d7-449f-a50d-936b9e173285","company_id":"aa372131-86ce-432a-af45-e2b42a79ba29","title":"Applied AI Researcher, Multi-Agent Systems","slug":"applied-ai-researcher-multi-agent-systems-4abe2461","description":"ABOUT DISTYL AI\n\nDistyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.\n\nWe research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.\n\nDistyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s. The results reflect this approach: a 100% production deployment success rate for our customers and one of the few enterprise AI companies to run a profitable business.\n\n\n\n\nWHAT WE ARE LOOKING FOR\n\nAt Distyl we’re pushing the envelope of AI utilization in enterprise. This requires creative researchers who don’t just want to drive incremental improvements on benchmarks or optimize an existing process but instead are looking to creatively redefine how software is used.\n\nOur researchers come from many academic backgrounds but have strong research track records, operate in an AI-native way, and would be bored staying on the rails of a traditional research org.\n\n \n\n\nKEY RESPONSIBILITIES\n\n - The Multi-Agent Systems team focuses on designing architectures in which multiple agents coordinate to solve problems that require structured interaction across multiple reasoning processes. Researchers build systems that structure communication, route information, and coordinate decision-making across agents operating with different views of the problem\n\n - Researchers in Multi-Agent Systems investigate the interaction patterns that govern how agents collaborate. They study how agents exchange information, critique and refine each other’s reasoning, and coordinate execution across complex workflows. Their work identifies the mechanics behind effective communication, delegation, and coordination, in effect establishing the design language for how systems of agents can operate as cohesive, high-performing teams, with capabilities that arise from interaction rather than individual performance.\n\n \n\n\nWHAT WE REQUIRE\n\n - Built or studied systems where multiple agents collaborate through structured communication, delegation, critique, or iterative coordination.\n\n - Experience with agent orchestration, communication protocols, evaluator agents, or systems where multiple agents interact to exchange information, critique reasoning, and coordinate decisions over time\n\n - Experience with research in related fields, such as multi-agent reinforcement learning (MARL), graph neural networks (GNNs), knowledge graphs, mixed-initiative planning, etc.\n\n - Excited about making foundational advancements in how agents coordinate, reason and collaborate\n\n - Proven Track Record of Research Results: Whether you’ve published in top journals, posted amazing work on twitter, or somewhere else we want to see what you've done.\n\n - Uses AI Every Day: Before you can revolutionize someone else’s workflow, you need to revolutionize yours. You should be using tools like ChatGPT, Cursor, and Perplexity to accelerate your workflow.\n\n - Strong Programming and Data Analysis Skills: While you might not consider yourself a software engineer you need to be able to build prototypes of your ideas and then perform the experiments to prove the effectiveness to a F500 Head of AI.\n\n - Biases Towards Showing vs Telling: Our customers want to see the power of AI today vs discuss the most elegant idea that will take 5 years to realize.\n   \n    \n\n\nWHAT WE OFFER\n\n - The base salary range for this role is $150K – $250K, depending on experience, location, and level. In addition to base compensation, this role is eligible for meaningful equity, along with a comprehensive benefits package\n\n - 100% covered medical, dental, and vision for employees and dependents\n\n - 401(k) with additional perks (e.g., commuter benefits, in‑office lunch)\n\n - Access to state‑of‑the‑art models, generous usage of modern AI tools, and real‑world business problems\n\n - Ownership of high‑impact projects across top enterprises\n\n - A mission‑driven, fast‑moving culture that prizes curiosity, pragmatism, and excellence\n\nDistyl has offices in San Francisco and New York. This role follows a hybrid collaboration model with 3+ days per week (Tuesday–Thursday) in‑office.\n\n\n\n#LI-Hybrid\n\n\n\nWe believe diverse perspectives make our work stronger and more impactful. We are an equal opportunity employer and evaluate all appl","salary_min":150000,"salary_max":250000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["deep-learning","healthcare","agents","reinforcement-learning","research"],"apply_url":"https://jobs.ashbyhq.com/distyl/1a44c296-a732-4374-9f8c-a613b17ae37b/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-18T22:41:22.889Z","expires_at":"2026-06-29T14:17:46.927997Z","created_at":"2026-05-27T14:18:38.728992Z","updated_at":"2026-05-30T14:17:47.047265Z","company_name":"Distyl AI","company_slug":"distyl-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=distyl.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/657568b2-56d7-449f-a50d-936b9e173285"}],"page":1,"per_page":20,"total":796,"total_pages":40}