{"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL","slug":"research-engineer-performance-rl-2f0da25a","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL Teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:\n \n \n Developing systems that enable models to use computers effectively\n \n Advancing code generation through reinforcement learning\n \n Pioneering fundamental RL research for large language models\n \n Building scalable RL infrastructure and training methodologies\n \n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the Role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.\n You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:\n \n \n Invent, design and implement RL environments and evaluations.\n \n Conduct experiments and shape our research roadmap.\n \n Deliver your work into training runs.\n \n Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.\n \n You may be a good fit if you:\n \n \n Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).\n \n Have worked across the stack – kernels, model code, distributed systems.\n \n Know how to balance research exploration with engineering implementation.\n \n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have:\n \n \n Experience with reinforcement learning.\n \n Experience porting ML workloads between different types of accelerators.\n \n Familiarity with LLM training methodologies.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $350,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","gpu","code-generation","distributed-systems","search","jax","fine-tuning","llm"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","is_featured":true,"is_sticky":true,"status":"active","published_at":"2026-03-23T16:27:59Z","expires_at":"2026-06-29T14:00:21.52187Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-05-30T14:00:21.633054Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/48720738-0f4b-483d-9739-14039ae457d0"},{"id":"f47b2b52-9138-4056-a197-783873a96c39","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Voice Model","slug":"member-of-technical-staff-voice-model-5b5f6cb9","description":"ABOUT xAI \n xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. \n ABOUT THE ROLE:\n You will join the Grok Voice Model team to help build the world’s best voice AI. We deliver smooth, natural, low-latency spoken interactions — expressive, multilingual, and reliable across devices and real-time scenarios. We own the full training pipeline: massive data curation, premium audio processing, frontier speech-language pre-training, and intensive post-training to push quality, speed, and stability to the limit.\n Our goal: make talking to AI feel like conversing with the most charming, kind, and knowledgeable person imaginable. We’re seeking exceptionally smart, execution-oriented engineers to help us get there.\n RESPONSIBILITIES:\n \n Design and execute large-scale speech data curation and processing pipelines, including collection of diverse real-world audio, synthetic data generation, and automated annotation workflows to enable high-quality model training and evaluation.\n Work on pre-training and post-training of speech-language models, with targeted enhancements through supervised fine-tuning, reinforcement learning, and other techniques to ensure Grok Voice responses are accurate, factually grounded, natural and idiomatic in spoken style, conversational in tone, and fluent across multiple languages.\n Build and iterate a comprehensive evaluation framework covering objective metrics (accuracy, quality, latency, expressiveness), human preference studies, content factuality assessments, real-time interaction quality, and experimentation infrastructure to measure and improve performance.\n Work closely with product teams to integrate voice models into applications and real-time environments, define spoken interaction specifications, and handle the full lifecycle from prototype to global-scale deployment for stable, low-latency, delightful voice experiences.\n \n BASIC QUALIFICATIONS:\n \n Python expert with deep proficiency in writing clean, efficient code for AI/ML systems.\n Hands-on experience processing large-scale datasets using tools like Spark and Ray for cleaning, augmentation, and feature extraction.\n Proficiency in pre-training and post-training speech-language models using JAX/PyTorch, including supervised fine-tuning, reinforcement learning, and optimizations for accuracy, factuality, natural spoken style, detail, and multilingual fluency.\n Ability to set up and run rigorous evaluation pipelines: objective metrics, human preference studies, content factuality checks, and iterative A/B testing to drive model improvements.\n Experience building or working with large-scale distributed training and inference systems on Kubernetes.\n Proactive, self-driven attitude — ready to grind in a fast-paced, high-caliber team to deliver outstanding voice AI experiences.\n \n COMPENSATION AND BENEFITS:\n $150,000 - $450,000 USD\n Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n xAI is an equal opportunity employer. For details on data processing, view our  Recruitment Privacy Notice .","salary_min":150000,"salary_max":450000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["speech","reinforcement-learning","pre-training","pytorch","fine-tuning","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5051966007","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-16T20:39:18Z","expires_at":"2026-06-29T14:02:58.935925Z","created_at":"2026-04-13T09:38:43.3144Z","updated_at":"2026-05-30T14:02:59.041832Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f47b2b52-9138-4056-a197-783873a96c39"},{"id":"f8c6c621-b459-40f6-b41d-0baa191734ff","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Lead, Training Insights","slug":"research-lead-training-insights-6091f430","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n As a Research Lead on the Training Insights team, you'll develop the strategy for, and lead execution on, how we measure and characterize model capabilities across training and deployment. This is a hands-on leadership role: you'll drive original research into new evaluation methodologies while leading a small team of researchers and research engineers doing the same.\n Your work will span the full lifecycle of model development. You'll research and build new long-horizon evaluations that test the boundaries of what our models can achieve, develop novel approaches to measuring emerging capabilities, and deepen our understanding of how those capabilities develop — both during production RL training and after. You'll also take a cross-organizational view, working across Reinforcement Learning, Pretraining, Inference, Product, Alignment, Safeguards, and other teams to map the landscape of model evaluations at Anthropic and identify critical gaps in coverage.\n This role carries significant visibility and impact. You'll help shape the evaluation narrative for model releases, contributing directly to how Anthropic communicates about its models to both internal and external audiences. Done well, you will change how the industry measures and understands model capabilities, significantly furthering our safety mission.  \n Responsibilities:  \n \n Build new novel and long-horizon evaluations\n Develop novel measurement approaches for understanding how model capabilities emerge and evolve during RL training\n Lead strategic evaluation coverage across the company\n Shape the evaluation narrative for model releases\n Lead and mentor a small team of researchers and research engineers, setting research direction and fostering a culture of rigorous, creative research\n Design evaluation frameworks that balance scientific rigor with the practical demands of production training schedules\n Build and maintain relationships across Anthropic's research organization to ensure evaluation insights inform training and deployment decisions\n Contribute to the broader research community through publications, open-source contributions, or external engagement on evaluation best practices\n \n You may be a good fit if you:  \n \n Have significant experience designing and running evaluations for large language models or similar complex ML systems\n Have led technical projects or teams, either formally or through sustained ownership of critical research directions\n Are equally comfortable designing experiments and writing code—you can move between research and implementation fluidly\n Think strategically about what to measure and why, not just how to measure it\n Can synthesize information across multiple teams and workstreams to form a coherent picture of model capabilities\n Communicate complex technical findings clearly to both technical and non-technical audiences\n Are results-oriented and thrive in fast-paced environments where priorities shift based on research findings\n Care deeply about AI safety and want your work to directly influence how capable AI systems are developed and deployed\n \n Strong candidates may also have:  \n \n Experience building evaluations for long-horizon or agentic tasks\n Deep familiarity with Reinforcement Learning training dynamics and how model behavior changes during training\n Published research in machine learning evaluation, benchmarking, or related areas\n Experience with safety evaluation frameworks and red teaming methodologies\n Background in psychometrics, experimental psychology, or other measurement-focused disciplines\n A track record of communicating evaluation results to inform high-stakes decisions about model development or deployment\n Experience managing or mentoring researchers and engineers\n \n Representative projects:  \n \n Designing and implementing a suite of long-horizon evaluations that test model capabilities on tasks requiring sustained reasoning, planning, and tool use over extended interactions\n Building systems to track capability development across RL training checkpoints, surfacing insights about when and how specific capabilities emerge\n Conducting a cross-org audit of evaluation coverage, identifying blind spots, and prioritizing new evaluations to fill critical gaps across Pretraining, RL, Inference, and Product\n Developing the evaluation methodology and narrative for a major model release, working with research leads and communications to clearly characterize model capabilities and limitations\n Researching and prototyping novel evaluation approaches for capabilities that are difficult to measure with existing benchmarks\n Leading a team","salary_min":850000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","pre-training","agents","alignment","search","llm","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5139654008","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-06T17:15:29Z","expires_at":"2026-06-29T14:00:22.960238Z","created_at":"2026-04-13T09:36:01.625992Z","updated_at":"2026-05-30T14:00:23.075652Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f8c6c621-b459-40f6-b41d-0baa191734ff"},{"id":"09d0acb5-52de-4a76-88c6-0eb844785025","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Research Scientist - RL Training","slug":"research-scientist-rl-training-ffdbae39","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes between 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n ABOUT THE ROLE  \n We're looking for a Research Scientist to work on reinforcement learning for training and aligning large language models. This is a foundational research role focused on one of the most consequential open data problems in AI: how to generate the data, reward signals, and training procedures that steer LLM behavior in reliable and generalizable directions — and a core capability that directly differentiates Snorkel's data-as-a-service offering. \n You'll work closely with Snorkel's research, engineering, and delivery teams to advance our RL data capabilities — translating research ideas into the preference datasets, reward models, and RL-ready corpora we produce for frontier AI labs, and contributing to a research agenda that is central to Snorkel's long-term differentiation as a provider of bespoke training data. \n MAIN RESPONSIBILITIES  \n \n Research and implement reinforcement learning techniques — including GRPO, RLHF, RLAIF, DPO, and reward modeling — and translate them into data products (preference datasets, reward signals, verifiable rewards) that customers can use to train and fine-tune large language models. \n Design and build data pipelines that generate high-quality training signal for RL workflows, including AI-assisted data annotation and curation data pipelines to improve model generalization to unseen benchmarks . \n Prototype and iterate on end-to-end RL training recipes that inform what data Snorkel ships as part of its data-as-a-service deliveries. \n Work closely with research scientists, ML engineers, and delivery teams to translate RL research into customer-ready data products.\n Stay current with the latest developments in large-scale muli-node LLM training, alignment research, and scalable RL methods (on complex environments such as Terminal-Bench), bringing relevant advances into Snorkel's data-as-a-service approach.\n Contribute to Snorkel's research publications and internal knowledge base in RL and model training.\n \n PREFERRED QUALIFICATIONS  \n \n Deep expertise in reinforcement learning from human or AI feedback, reward modeling and credit attribution ideally with a clear perspective on what data makes these techniques work. \n Experience training or fine-tuning 30B+ large language models at scale, including familiarity with distributed training infrastructure. \n Strong proficiency in Python and ML frameworks, especially PyTorch and HuggingFace and hands-on experience with RL frameworks such as Verl and SkyRL. \n Solid software engineering fundamentals — you can build research prototypes that others can run, extend, and integrate into data production workflows. \n Familiarity with ML infrastructure and cloud platforms and tools (AWS, GCP, Kubernetes, Slurm, etc.); experience with large-scale RL training pipelines a strong plus. \n Comfort operating in a high-iteration environment with open-ended research questions and shifting, customer-driven technical constraints. \n Ph.D. in machine learning, reinforcement learning, or a related field strongly preferred; exceptional industry experience considered. \n Salary Range \n $200,000 — $325,000 USD \n Be Your Best at Snorkel \n Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.\n Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and haras","salary_min":200000,"salary_max":325000,"location":"Redwood City, CA","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["alignment","distributed-systems","pytorch","fine-tuning","generative-ai","data-pipeline","llm","reinforcement-learning"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6009496004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T21:22:40Z","expires_at":"2026-06-29T14:03:05.747327Z","created_at":"2026-05-30T14:03:05.857458Z","updated_at":"2026-05-30T14:03:05.857458Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/09d0acb5-52de-4a76-88c6-0eb844785025"},{"id":"14a818b5-1068-4d53-8e01-2106c013d919","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Software Engineer, Operational/ Process Efficiency ","slug":"software-engineer-operational-process-efficiency-0675432b","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 This role is at the intersection of robotics and machine learning, driving the next generation of operational efficiency for Waymo's rapidly expanding autonomous fleet. You will lead efforts to generalize complex depot operations—such as exterior cleaning, sensor calibration, and maintenance checks—using advanced robotics. Key work involves leveraging foundation models, reinforcement learning, simulation, and integrating ML models in production at scale. You will interface closely with operations teams to translate real-world needs into robust, working solutions.\n This role follows a hybrid work schedule and reports to a Director, Hardware and Sensors. \n You will: \n \n Drive the automation of the hardware lifecycle for critical sensors (lidar, radar, cameras) and compute modules.\n Develop and deploy agentic systems and foundation models to streamline workflows between internal teams and contract manufacturers.\n Identify opportunities to apply AI to manufacturing, installation, and troubleshooting processes to increase operational velocity.\n Interface with a diverse set of stakeholders, including hardware design engineers, failure analysis engineers, and diagnostic teams, to translate physical requirements into technical specifications.\n Bridge the gap between experimental ML models and high-scale production environments.\n \n You have: \n \n A Masters or PhD in Machine Learning, Computer Science, or a related technical field.\n A proven track record of delivering working engineering solutions, balancing scientific rigor with production needs.\n Experience in training, evaluating, and deploying machine learning models at scale.\n Strong communication skills and the ability to collaborate across multidisciplinary teams (from field technicians to hardware designers).\n \n We prefer: \n \n Hands-on experience or deep familiarity with agentic tools and frameworks.\n Experience working with large-scale foundation models (LLMs, VLMs) and fine-tuning them for specialized domains.\n Background in automating industrial or hardware-centric workflows.\n Familiarity with hardware diagnostics, failure analysis, or manufacturing processes.\n \n  \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":"hybrid","job_type":"full-time","experience_level":"mid","tags":["agents","generative-ai","robotics","autonomous-vehicles","llm","reinforcement-learning","fine-tuning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7926526","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T20:26:39Z","expires_at":"2026-06-29T14:04:30.317025Z","created_at":"2026-05-30T14:04:30.42607Z","updated_at":"2026-05-30T14:04:30.42607Z","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/14a818b5-1068-4d53-8e01-2106c013d919"},{"id":"2f82717a-ca5c-44ec-afbc-871db9888784","company_id":"f36ec848-cb19-4b95-a680-6733e58086c0","title":"Director, Data Science","slug":"director-data-science-e0c2bfe0","description":"May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. Based in Ann Arbor, Michigan, May develops and deploys autonomous vehicles (AVs) powered by our innovative Multi-Policy Decision Making (MPDM) technology that literally reimagines the way AVs think. Our vehicles do more than just drive themselves - they provide value to communities, bridge public transit gaps and move people where they need to go safely, easily and with a lot more fun. We’re building the world’s best autonomy system to reimagine transit by minimizing congestion, expanding access and encouraging better land use in order to foster more green, vibrant and livable spaces. Since our founding in 2017, we’ve given more than 500,000 autonomous rides to real people around the globe. And we’re just getting started. We’re hiring people who share our passion for building the future, today, solving real-world problems and seeing the impact of their work. Join us. \n Job Summary \n May Mobility is entering an exciting phase of growth as we expand our autonomous transit and mobility services across the country. Founded in 2017 by a team of experienced roboticists, perception, behavior, AI, and software engineers, we operate driverless transit shuttles in real communities — not as a research demonstration, but as a daily-service product that people rely on to get to work, school, and home.\n The Director, Data Science will lead the team responsible for turning the data generated by our fleet, simulation environment, and ML systems into the insights, evaluations, and decisions that make our autonomous service safer, more efficient, and ready to scale into new cities. You will own data science across simulation and synthetic data, perception and planning ML evaluation, fleet operations analytics, and the data infrastructure that supports them. You will partner directly with Engineering, Product, Operations, and Safety leadership to set measurement standards, define release criteria, and translate frontline operating data into the next generation of our autonomy stack.\n This is a leadership role for someone who has scaled a data science function inside a hard-tech environment, who is comfortable making engineering and product tradeoffs alongside their team, and who sees the gap between research-grade ML and production transit-grade ML as the most interesting problem in the industry today.\n Essential Responsibilities \n \n Set and own the data science strategy across simulation and synthetic data, ML evaluation (perception, prediction, planning), fleet operations analytics, and the data platform that supports them; translate that strategy into a 12–24 month roadmap with measurable milestones.\n Lead, grow, and develop a team of senior data scientists, ML engineers, and front-line managers; recruit from a small expert pool, calibrate the bar, and build a hiring brand that allows May Mobility to win against AV, robotics, and AI competitors.\n Partner with Engineering, Product, Safety, and Operations leaders to define release criteria, performance metrics, and ODD-expansion gates; use data to make the business case for what we deploy, where, and when.\n Drive ML and analytics applications end-to-end: dataset curation, scenario coverage, modeling, offboard evaluation, productionization, and continuous monitoring of fleet performance in the wild.\n Establish measurement and experimentation standards across the company — including before/after analyses for stack changes, A/B-style comparisons in simulation, and statistically credible reporting on real-world incidents.\n Lead team-wide quality activities including design and code reviews; hold the bar on engineering rigor for production data science systems.\n Track and trend technical performance of the autonomy stack in the field; surface root causes, prioritize fixes with engineering, and represent fleet-data findings to executives, regulators, and partners.\n Provide technical guidance to Engineering and Operations leaders on issue diagnosis, resolution, and the ML changes most likely to move our key safety and service metrics.\n Represent May Mobility's data science work externally where appropriate — through publications, conference talks, partner reviews, and recruiting.\n \n Skills and Abilities \n Success in this role typically requires the following competencies: \n \n Autonomy Data Expertise. Can reason fluently about the data produced by a modern AV stack — sensor logs, perception outputs, planning traces, simulator results, and operational telemetry — and can identify which signals matter for which decisions.\n Hands-On Technical Depth. Has personally shipped production ML or analytics systems within the last 3–5 years and is credible in code review and design review with senior engineers and scientists.\n Cross-Functional Translator. Can explain a complex ML or statistical finding to engineering, product, and executive audiences; and ","salary_min":217000,"salary_max":312000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["robotics","healthcare","distributed-systems","pytorch","computer-vision","tensorflow","reinforcement-learning","autonomous-vehicles"],"apply_url":"https://job-boards.greenhouse.io/maymobility/jobs/8561428002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T21:54:47Z","expires_at":"2026-06-29T14:17:06.3175Z","created_at":"2026-05-28T14:18:43.046233Z","updated_at":"2026-05-30T14:17:06.431533Z","company_name":"May Mobility","company_slug":"may-mobility","company_logo_url":"https://www.google.com/s2/favicons?domain=maymobility.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2f82717a-ca5c-44ec-afbc-871db9888784"},{"id":"41b3afd9-e8d0-4d82-9e8e-9149ad7c9147","company_id":"0bedcaf4-210e-4f52-95d5-a82be8aff446","title":"Sr Machine Learning Engineer, AI Research","slug":"sr-machine-learning-engineer-ai-research-866a2680","description":"Join the company that’s building the telemetry infrastructure for the AI era. At Cribl, we partner with IT and Security teams at many of the world’s biggest enterprises, including half of the Fortune 100, to bridge the gap between AI ambition and infrastructure reality. As the AI Platform for Telemetry, we give customers the choice, control, and flexibility to manage and analyze telemetry for both humans and agents, so they can build what’s next.\n We’re one of the fastest‑growing private companies and a leading player in a massive, fast‑moving market. With a global workforce, we’re remote‑first and grounded in a simple idea: software is a people business. Cribl is the place where curious, collaborative people can do their best work, grow fast, and bring their full selves to the herd.\n Why You'll Love This Role \n You will work closely with the founding team and a group of highly-skilled engineers to shape the future of AI-enabled Security/Observability platforms. You will play a central role in bringing integrating cutting-edge AI/ML technologies to the Cribl Product suite to help solve real customer problems.  You will work closely with development partners and key stakeholders to iteratively design, develop, and deliver products and surfaces that will delight our customers.\n On top of it all you will have fun. \n Cribl strives to be a great place to work for everyone.\n As An Active Member Of Our Team, You Will... \n \n Design, train, and evaluate machine learning models across a range of research and applied AI initiatives\n Run rapid, iterative experiments to test hypotheses and surface insights that drive model improvements\n Collaborate closely with researchers and engineers to translate cutting-edge academic advances into practical, production-ready systems\n Build and maintain robust ML pipelines for data ingestion, feature engineering, model training, and evaluation\n Optimize model performance through fine-tuning, hyperparameter search, and architecture experimentation\n Contribute to a culture of rigorous experimentation; tracking results, documenting findings, and sharing learnings with the broader team\n Stay current with the latest developments in ML and AI research, and proactively identify opportunities to apply them\n This position may require stand-by, on-call, or off-hours duties during critical research or deployment milestones\n \n If You've Got It - We Want It \n \n Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field with 4+ years of industry or research experience (Master's or PhD a plus)\n Deep hands-on experience training and evaluating ML models, including language models\n Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow\n Familiarity with MLOps tooling and infrastructure (e.g., MLflow, Weights \u0026 Biases, Kubeflow, or similar)\n Solid understanding of modern NLP, computer vision, and/or reinforcement learning techniques\n Strong ability to move fast without sacrificing rigor; you know when to prototype and when to productionize\n Excellent communication skills with the ability to clearly present experimental results to both technical and non-technical stakeholders\n \n #LI-Tag #LI-Remote\n The salary for this role is dependent on geographic location and will be based on the individual candidate's job-related knowledge, skills, and experience. In addition to base salary, for sales and some sales-adjacent roles, employees are eligible to earn incentive compensation (commission). For all other roles, employees are eligible to participate in the Cribl Corporate Bonus Program. In addition to a competitive salary, Cribl also offers a generous benefits package which includes health, dental, vision, short-term disability, and life insurance, paid holidays and paid time off, a fertility treatment benefit, 401(k), and equity.\n Base Salary Range\n $185,000 — $215,000 USD \n Bring Your Whole Self Diversity drives innovation, enables better decisions to support our customers, and inspires change for the better. We’re building a culture where differences are valued and welcomed, and we work together to bring out the best in each other. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or any other applicable legally protected characteristics in the location in which the candidate is applying. \n Interested in joining the Cribl herd? Learn more about the smartest, funniest, most passionate goats you’ll ever meet at cribl.io/about-us .","salary_min":185000,"salary_max":215000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["nlp","tensorflow","computer-vision","mlops","pytorch","reinforcement-learning","fine-tuning","research"],"apply_url":"https://cribl.io/job-detail/?gh_jid=5979543004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T18:02:31Z","expires_at":"2026-06-29T14:18:07.512926Z","created_at":"2026-05-28T14:19:42.491471Z","updated_at":"2026-05-30T14:18:07.623902Z","company_name":"Cribl","company_slug":"cribl","company_logo_url":"https://www.google.com/s2/favicons?domain=cribl.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/41b3afd9-e8d0-4d82-9e8e-9149ad7c9147"},{"id":"fa191a7a-632b-472c-acb7-b7360a7925f8","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Director, Prediction and ML Planning","slug":"director-prediction-and-ml-planning-360f2a66","description":"About Motional: \n Motional is a public transit and autonomous vehicle pioneer, developing Level 4 driverless vehicles that are changing the way the world moves. At the heart of our mission is the Autonomy organization, where we solve some of the most complex engineering and artificial intelligence challenges of our generation. Mission Summary: \n Motional is seeking a visionary, technically deep Director of Behaviors to lead our machine learning-based Prediction and Planning teams. In this role, you will sit at the intersection of intent forecasting and ego-vehicle decision-making. You will be directly responsible for leading multiple engineering sub-teams, setting the technical roadmap for our next-generation behavior stack, and pioneering the shift toward state-of-the-art end-to-end models that execute joint prediction and planning.\n As a senior leader in the Autonomy organization, you will not only drive technical breakthroughs but will also scale and nurture a world-class AI organization in a sustainable, inclusive, and highly collaborative fashion. Core Responsibilities: \n \n Strategic Leadership: Oversee and unify the machine learning-based Prediction and Motion Planning teams. Establish a clear, aggressive, yet sustainable technical roadmap that transitions our stack towards a unified (fully learnt) Large Driving Model performing joint prediction and planning.\n Technical Direction: Stay at the absolute frontier of AI research and define the technical roadmap for developing state-of-the-art imitation learning (IL) and reinforcement learning (RL) approaches to advance end-to-end learnt planning. Guide the team in exploring and incorporating modern paradigms like Vision-Language-Action models (VLAs) to improve the vehicle's semantic understanding, reasoning, and zero-shot generalization capabilities in complex urban environments. \n Organizational Growth: Lead, mentor, and scale multiple sub-teams of machine learning engineers and researchers. Implement sustainable engineering practices that prevent burnout, promote psychological safety, and ensure high technical velocity.\n Cross-Functional Collaboration: Partner closely with Perception, Infrastructure and Systems Engineering to ensure the Large Driving Model seamlessly integrates onto the vehicle platform and meets rigorous safety and real-time performance standards. \n \n Required Qualifications \u0026 Experience: \n \n Proven Leadership: 5+ years of experience managing high-performing engineering teams, with at least 3+ years of experience managing multiple sub-teams within an autonomous systems, robotics, or advanced AI organization.\n Sustainable Scaling: Demonstrated track record of growing an engineering organization sustainably—balancing technical debt, architectural scalability, and team well-being.\n ML Behavior Expertise: Deep theoretical and practical proficiency in machine learning applied to robotics behaviors. Advanced expertise in Imitation Learning and Reinforcement Learning for decision-making. Strong understanding of the full lifecycle from research to vehicle deployment.\n Unified Architectures: Proven experience guiding teams toward building integrated models (e.g., trajectory forecasting joint with ego-policy generation) rather than decoupled, sequential pipelines.\n Modern AI Paradigms: Strong familiarity with multimodal foundational AI models, specifically Vision-Language-Action models (VLAs) .\n Educational Background: M.S. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related quantitative field with a heavy focus on Machine Learning. \n \n Preferred Qualifications: \n \n Experience building and scaling up LLMs/VLMs/VLAs and successfully deploying to production.\n A strong footprint in the AI/robotics research community (CVPR, ICCV, NeurIPS, ICRA, IROS publications), with a willingness to publish future work.\n Experience building large-scale data pipelines and training infrastructure required to train large driving models.\n \n \n  We encourage a hybrid schedule with in-office time at one of our locations in Boston or Pittsburgh to support collaboration, or this role can be fully remote. \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process.  \n Candidates for certain positions are eligible to participate in Motional’s benefits program. Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. \n Salary Rang","salary_min":288000,"salary_max":396000,"location":"Remote (US)","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["autonomous-vehicles","llm","reinforcement-learning","robotics","data-pipeline"],"apply_url":"https://motional.com/open-positions/?gh_jid=7749539003#/7749539003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T14:37:33Z","expires_at":"2026-06-29T14:05:57.689923Z","created_at":"2026-05-28T14:07:27.811425Z","updated_at":"2026-05-30T14:05:57.80649Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fa191a7a-632b-472c-acb7-b7360a7925f8"},{"id":"a81eda03-7e82-45be-919e-39f563f2c24d","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Director, Prediction and ML Planning","slug":"director-prediction-and-ml-planning-ec32b5bd","description":"About Motional: \n Motional is a public transit and autonomous vehicle pioneer, developing Level 4 driverless vehicles that are changing the way the world moves. At the heart of our mission is the Autonomy organization, where we solve some of the most complex engineering and artificial intelligence challenges of our generation. Mission Summary: \n Motional is seeking a visionary, technically deep Director of Behaviors to lead our machine learning-based Prediction and Planning teams. In this role, you will sit at the intersection of intent forecasting and ego-vehicle decision-making. You will be directly responsible for leading multiple engineering sub-teams, setting the technical roadmap for our next-generation behavior stack, and pioneering the shift toward state-of-the-art end-to-end models that execute joint prediction and planning.\n As a senior leader in the Autonomy organization, you will not only drive technical breakthroughs but will also scale and nurture a world-class AI organization in a sustainable, inclusive, and highly collaborative fashion. Core Responsibilities: \n \n Strategic Leadership: Oversee and unify the machine learning-based Prediction and Motion Planning teams. Establish a clear, aggressive, yet sustainable technical roadmap that transitions our stack towards a unified (fully learnt) Large Driving Model performing joint prediction and planning.\n Technical Direction: Stay at the absolute frontier of AI research and define the technical roadmap for developing state-of-the-art imitation learning (IL) and reinforcement learning (RL) approaches to advance end-to-end learnt planning. Guide the team in exploring and incorporating modern paradigms like Vision-Language-Action models (VLAs) to improve the vehicle's semantic understanding, reasoning, and zero-shot generalization capabilities in complex urban environments. \n Organizational Growth: Lead, mentor, and scale multiple sub-teams of machine learning engineers and researchers. Implement sustainable engineering practices that prevent burnout, promote psychological safety, and ensure high technical velocity.\n Cross-Functional Collaboration: Partner closely with Perception, Infrastructure and Systems Engineering to ensure the Large Driving Model seamlessly integrates onto the vehicle platform and meets rigorous safety and real-time performance standards. \n \n Required Qualifications \u0026 Experience: \n \n Proven Leadership: 5+ years of experience managing high-performing engineering teams, with at least 3+ years of experience managing multiple sub-teams within an autonomous systems, robotics, or advanced AI organization.\n Sustainable Scaling: Demonstrated track record of growing an engineering organization sustainably—balancing technical debt, architectural scalability, and team well-being.\n ML Behavior Expertise: Deep theoretical and practical proficiency in machine learning applied to robotics behaviors. Advanced expertise in Imitation Learning and Reinforcement Learning for decision-making. Strong understanding of the full lifecycle from research to vehicle deployment.\n Unified Architectures: Proven experience guiding teams toward building integrated models (e.g., trajectory forecasting joint with ego-policy generation) rather than decoupled, sequential pipelines.\n Modern AI Paradigms: Strong familiarity with multimodal foundational AI models, specifically Vision-Language-Action models (VLAs) .\n Educational Background: M.S. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related quantitative field with a heavy focus on Machine Learning. \n \n Preferred Qualifications: \n \n Experience building and scaling up LLMs/VLMs/VLAs and successfully deploying to production.\n A strong footprint in the AI/robotics research community (CVPR, ICCV, NeurIPS, ICRA, IROS publications), with a willingness to publish future work.\n Experience building large-scale data pipelines and training infrastructure required to train large driving models.\n \n \n  We encourage a hybrid schedule with in-office time at one of our locations in Boston or Pittsburgh to support collaboration, or this role can be fully remote. \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process.  \n Candidates for certain positions are eligible to participate in Motional’s benefits program. Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. \n Salary Rang","salary_min":288000,"salary_max":396000,"location":"Pittsburgh, PA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","autonomous-vehicles","robotics","llm","data-pipeline"],"apply_url":"https://motional.com/open-positions/?gh_jid=7749537003#/7749537003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T14:37:33Z","expires_at":"2026-06-29T14:05:57.776906Z","created_at":"2026-05-28T14:07:27.895574Z","updated_at":"2026-05-30T14:05:57.889935Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a81eda03-7e82-45be-919e-39f563f2c24d"},{"id":"3fa85144-87ad-48b5-b151-9737480face6","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Director, Prediction and ML Planning","slug":"director-prediction-and-ml-planning-ecc5bb9c","description":"About Motional: \n Motional is a public transit and autonomous vehicle pioneer, developing Level 4 driverless vehicles that are changing the way the world moves. At the heart of our mission is the Autonomy organization, where we solve some of the most complex engineering and artificial intelligence challenges of our generation. Mission Summary: \n Motional is seeking a visionary, technically deep Director of Behaviors to lead our machine learning-based Prediction and Planning teams. In this role, you will sit at the intersection of intent forecasting and ego-vehicle decision-making. You will be directly responsible for leading multiple engineering sub-teams, setting the technical roadmap for our next-generation behavior stack, and pioneering the shift toward state-of-the-art end-to-end models that execute joint prediction and planning.\n As a senior leader in the Autonomy organization, you will not only drive technical breakthroughs but will also scale and nurture a world-class AI organization in a sustainable, inclusive, and highly collaborative fashion. Core Responsibilities: \n \n Strategic Leadership: Oversee and unify the machine learning-based Prediction and Motion Planning teams. Establish a clear, aggressive, yet sustainable technical roadmap that transitions our stack towards a unified (fully learnt) Large Driving Model performing joint prediction and planning.\n Technical Direction: Stay at the absolute frontier of AI research and define the technical roadmap for developing state-of-the-art imitation learning (IL) and reinforcement learning (RL) approaches to advance end-to-end learnt planning. Guide the team in exploring and incorporating modern paradigms like Vision-Language-Action models (VLAs) to improve the vehicle's semantic understanding, reasoning, and zero-shot generalization capabilities in complex urban environments. \n Organizational Growth: Lead, mentor, and scale multiple sub-teams of machine learning engineers and researchers. Implement sustainable engineering practices that prevent burnout, promote psychological safety, and ensure high technical velocity.\n Cross-Functional Collaboration: Partner closely with Perception, Infrastructure and Systems Engineering to ensure the Large Driving Model seamlessly integrates onto the vehicle platform and meets rigorous safety and real-time performance standards. \n \n Required Qualifications \u0026 Experience: \n \n Proven Leadership: 5+ years of experience managing high-performing engineering teams, with at least 3+ years of experience managing multiple sub-teams within an autonomous systems, robotics, or advanced AI organization.\n Sustainable Scaling: Demonstrated track record of growing an engineering organization sustainably—balancing technical debt, architectural scalability, and team well-being.\n ML Behavior Expertise: Deep theoretical and practical proficiency in machine learning applied to robotics behaviors. Advanced expertise in Imitation Learning and Reinforcement Learning for decision-making. Strong understanding of the full lifecycle from research to vehicle deployment.\n Unified Architectures: Proven experience guiding teams toward building integrated models (e.g., trajectory forecasting joint with ego-policy generation) rather than decoupled, sequential pipelines.\n Modern AI Paradigms: Strong familiarity with multimodal foundational AI models, specifically Vision-Language-Action models (VLAs) .\n Educational Background: M.S. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or a related quantitative field with a heavy focus on Machine Learning. \n \n Preferred Qualifications: \n \n Experience building and scaling up LLMs/VLMs/VLAs and successfully deploying to production.\n A strong footprint in the AI/robotics research community (CVPR, ICCV, NeurIPS, ICRA, IROS publications), with a willingness to publish future work.\n Experience building large-scale data pipelines and training infrastructure required to train large driving models. \n \n  We encourage a hybrid schedule with in-office time at one of our locations in Boston or Pittsburgh to support collaboration, or this role can be fully remote. \n The salary range for this role is an estimate based on a wide range of compensation factors including but not limited to specific skills, experience and expertise, role location, certifications, licenses, and business needs. The estimated compensation range listed in this job posting reflects base salary only. This role may include additional forms of compensation such as a bonus or company equity. The recruiter assigned to this role can share more information about the specific compensation and benefit details associated with this role during the hiring process.  \n Candidates for certain positions are eligible to participate in Motional’s benefits program. Motional’s benefits include but are not limited to medical, dental, vision, 401k with a company match, health saving accounts, life insurance, pet insurance, and more. \n Salary Range","salary_min":288000,"salary_max":396000,"location":"Boston, MA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","reinforcement-learning","llm","autonomous-vehicles","robotics"],"apply_url":"https://motional.com/open-positions/?gh_jid=7749522003#/7749522003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T14:37:32Z","expires_at":"2026-06-29T14:05:57.612382Z","created_at":"2026-05-28T14:07:27.983518Z","updated_at":"2026-05-30T14:05:57.722175Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3fa85144-87ad-48b5-b151-9737480face6"},{"id":"c96f95a6-0aa8-42c2-9fd5-75a8b7173a25","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Principal Machine Learning Engineer- LLM Fine-tuning and Optimization ","slug":"principal-machine-learning-engineer-llm-fine-tuning-and-optimization-bfee7362","description":"Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. \n Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.\n The Community You Will Join:  \n Machine Learning and Artificial Intelligence are at the heart of the Airbnb product. From Trust to Payments, and from Customer Service to Marketing we rely on ML to ensure that guests and hosts have the best possible experience with Airbnb. \n The CS AI product team is responsible for driving CSxAI (Customer Support x Artificial Intelligence) initiatives by adopting the Generative AI technologies to enable an intelligent, scalable and exceptional service experience. The team develops and enhances various AI models, ML services and tools including LLM fine-tuning, alignment and optimization, RAG/Search,  LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb. \n What you will do: \n As a principal machine learning engineer, you will be responsible for fine-tuning state-of-the-art LLMs for diverse use cases while optimizing models for high-performance deployment on Airbnb’s ML Infrastructure. You will partner with product managers, software engineers, data scientists and operation teams to brainstorm, design and develop AI products such as AI Assistant, Autonomous agent,  recommendation, travel planning, and many more products that make meaningful impacts in the world of travel. \n Your responsibilities:  \n \n Work with large scale structured and unstructured data; explore, experiment, build and continuously improve foundation models for Airbnb product, business and operational use cases.\n Create a multi-year tech roadmap that enables our team to stay on the leading edge of the rapidly evolving AI landscape and leverage the best in class technologies to deliver customer benefits.\n Continuously evaluate recent and upcoming large foundational models, ensuring the selection and refinement of the highest quality models for enhanced performance and efficiency.\n Hands-on prototype, develop and productionize LLM models and pipelines at scale, including both batch and real-time use cases.\n Drive key AI architectural decisions for products, and contribute to Airbnb’s ML platform architecture and strategy.\n \n Minimum Qualifications :\n \n PhD in Computer Science,  Machine Learning, Mathematics, Statistics, or related technical field.\n 10+ years of experience with developing machine learning models and products at scale from inception to business impact.\n Programming experience in Python and hands-on experience with frameworks such as PyTorch.\n Proven record of training, fine tuning, optimizing models and inference run-time\n Post-training experience in areas like data processing for fine-tuning; responsible LLMs; LLM alignment; reinforcement learning; efficient training and inference; language model evaluation; and/or multilingual and multimodal modeling.\n Or specialized experience in runtime optimizations, model quantization, compression, on-device inference, GPU inference, pytorch, kernel development\n \n Preferred Qualifications: \n \n PhD in AI, machine learning, data science, or related technical fields.\n \n Publications at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL). \n \n Customer Support Systems : Experience with AI technologies in customer support applications.\n Agile Practice for AI production : Experience with the entire AI product development lifecycle from incubation to production at scale, following agile practices in the Applied AI/ML domain.\n \n \n Infrastructure Acumen : Experience deploying and scaling business-critical AI services and driving architectural requirements on ML infrastructures\n \n Your Location: \n This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity.  Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list . If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. \n Our Commitment To Inclusion \u0026 Belonging: \n Airbnb is committed to working with the broadest talent","salary_min":292000,"salary_max":365000,"location":"United States","workplace":"onsite","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","fine-tuning","pytorch","generative-ai","llm","payments","agents","machine-learning"],"apply_url":"https://careers.airbnb.com/positions/7955579?gh_jid=7955579","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-24T23:37:28Z","expires_at":"2026-06-29T14:09:02.019884Z","created_at":"2026-05-27T14:09:19.150599Z","updated_at":"2026-05-30T14:09:02.131762Z","company_name":"Airbnb","company_slug":"airbnb","company_logo_url":"https://www.google.com/s2/favicons?domain=airbnb.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c96f95a6-0aa8-42c2-9fd5-75a8b7173a25"},{"id":"b40b33bc-6868-4ebb-ac4d-e56f85f945a1","company_id":"a355eb2f-63c3-4c0a-803d-bc2d8312b6d8","title":"Researcher, Context - Agent Post-Training","slug":"researcher-context-agent-post-training-528ac150","description":"About the Team\n\nThe Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve.\n\nWe define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste.\n\nOur team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use.\n\n\n\nAbout the Role\n\nWe believe that the final enabler for AGI is spending compute on context. As a Context Researcher on Agent Post-Training, you will scale compute spent on context. You will get to work in our frontier training stack on enabling the next paradigm of model training with a clear product interface for iterative deployment (Codex Chronicle). You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models.\n\n\n\nIn this role, you will:\n\n - Design and run experiments that improve scaling of compute on context.\n\n - Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis.\n\n - Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions.\n\n - Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements.\n\n - Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior.\n\n - Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs.\n\n - Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.\n\n - Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments.\n\n - Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes.\n\n\n\nYou might thrive in this role if you:\n\n - Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before.\n\n - Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems.\n\n - Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution.\n\n - Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with.\n\n - Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next.\n\n - Are comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group.\n\n - Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.\n\n - Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.\n\n\n\nCompensation Range: $250K - $380K USD\n\n\n\nAbout OpenAI\n\nOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. \n\nWe are an equal opportunity employer, and we do n","salary_min":250000,"salary_max":380000,"location":"San Francisco, CA","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","llm","agents","reinforcement-learning","research"],"apply_url":"https://jobs.ashbyhq.com/openai/5b1394e6-e133-4dc0-97aa-87a91e5d1b52/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T23:08:33.816Z","expires_at":"2026-06-29T14:01:00.439954Z","created_at":"2026-05-27T14:01:09.912882Z","updated_at":"2026-05-30T14:01:00.551597Z","company_name":"OpenAI","company_slug":"openai","company_logo_url":"https://www.google.com/s2/favicons?domain=openai.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b40b33bc-6868-4ebb-ac4d-e56f85f945a1"},{"id":"be23cbd4-5c9a-484b-a887-06083ee99bd5","company_id":"a355eb2f-63c3-4c0a-803d-bc2d8312b6d8","title":"Researcher, Connectors - Agent Post-Training","slug":"researcher-connectors-agent-post-training-e5a1f5ce","description":"About the Team\n\nThe Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve.\n\nWe define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste.\n\nOur team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use.\n\n\n\nAbout the Role\n\nAs a member of Agent Post-Training, Connectors, you will teach models how to interface with the top professional software using code. You will help train agents to use code, APIs, tools, and structured integrations to operate across applications like Slack, Google Workspace, GitHub, Notion, Linear, Salesforce, and other core systems of work. You will help enable models to take useful actions across a user’s digital context: finding information, updating systems, coordinating work, generating artifacts, and completing multi-step workflows through the tools teams already use.\n\nYou will train models to be supercharged by the world’s most important productivity and enterprise software, turning connected tools into a powerful action surface for our agents. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models.\n\n\n\nIn this role, you will:\n\n - Design and run experiments that improve agentic model behavior for complex software and plugins.\n\n - Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis.\n\n - Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions.\n\n - Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements.\n\n - Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior.\n\n - Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs.\n\n - Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.\n\n - Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments.\n\n - Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes.\n\n\n\nYou might thrive in this role if you:\n\n - Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before.\n\n - Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems.\n\n - Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution.\n\n - Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with.\n\n - Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next.\n\n - Are comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group.\n\n - Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.\n\n - Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.\n\nCompensation Ranges: $250K - $380K USD\n\n\n\nAbout OpenAI\n\nOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligen","salary_min":250000,"salary_max":380000,"location":"San Francisco, CA","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["llm","agents","data-pipeline","reinforcement-learning","research"],"apply_url":"https://jobs.ashbyhq.com/openai/d55af855-0d7b-4407-a27d-5fa9f894382c/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T23:08:05.419Z","expires_at":"2026-06-29T14:01:00.206702Z","created_at":"2026-05-27T14:01:09.65114Z","updated_at":"2026-05-30T14:01:00.312483Z","company_name":"OpenAI","company_slug":"openai","company_logo_url":"https://www.google.com/s2/favicons?domain=openai.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/be23cbd4-5c9a-484b-a887-06083ee99bd5"},{"id":"c8a45766-54d1-4818-ae4d-b8b7b937c1ae","company_id":"a355eb2f-63c3-4c0a-803d-bc2d8312b6d8","title":"Researcher, Artifacts - Agent Post-Training","slug":"researcher-artifacts-agent-post-training-0388a47d","description":"About the Team\n\nThe Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve.\n\nWe define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste.\n\nOur team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use.\n\n\n\nAbout the Role\n\nAs a member of Agent Post-Training, Artifacts, you will train frontier models to create polished, useful work products: documents, spreadsheets, slide decks, dashboards, reports, analyses, and other interactive or editable artifacts. You will help teach our models to move from a vague user goal to a finished artifact with strong structure, visual taste, domain judgment, correctness, and low latency. This work will require owning improvements across our post-training stack, including RL, data pipelines, graders, reward signals, evals, and behavioral analysis.\n\nYou will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models.\n\n\n\nIn this role, you will:\n\n - Design and run experiments that improve agentic model behavior for complex software and plugins..\n\n - Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis.\n\n - Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions.\n\n - Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements.\n\n - Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior.\n\n - Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs.\n\n - Improve the machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.\n\n - Take on cross-functional projects that touch model training, product infrastructure, and the production agent harness, such as multi-agent systems or training directly against production-like environments.\n\n - Debug hard failures in shipped or near-shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes.\n\n\n\nYou might thrive in this role if you:\n\n - Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before.\n\n - Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems.\n\n - Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution.\n\n - Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with.\n\n - Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next.\n\n - Are comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group.\n\n - Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous.\n\n - Want to train and ship the models that make agents genuinely useful for developers, enterprises, researchers, and everyday users.\n\n - Have some prior background in consulting, finance, marketing, operations, or data science.\n\n\n\nCompensation Range: $250K - $380K USD\n\n\n\nAbout OpenAI\n\nOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safel","salary_min":250000,"salary_max":380000,"location":"San Francisco, CA","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","agents","llm","reinforcement-learning","research"],"apply_url":"https://jobs.ashbyhq.com/openai/c701bf4a-3b17-4b14-895a-05f52be51cf8/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T23:06:48.091Z","expires_at":"2026-06-29T14:01:00.2812Z","created_at":"2026-05-27T14:01:09.751126Z","updated_at":"2026-05-30T14:01:00.393142Z","company_name":"OpenAI","company_slug":"openai","company_logo_url":"https://www.google.com/s2/favicons?domain=openai.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c8a45766-54d1-4818-ae4d-b8b7b937c1ae"},{"id":"d134135d-62d9-4aa9-acb7-410bbd77911c","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Sr. Staff Machine Learning Engineer, Content Ecosystem","slug":"sr-staff-machine-learning-engineer-content-ecosystem-3ffaf377","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n Pinterest works when the content ecosystem works: when people can reliably find ideas that feel inspiring, trustworthy, and actionable—and when the ecosystem continuously learns what to create, surface, and sustain next. In this Sr. Staff ML Engineer role, you’ll be the technical lead shaping how Pinterest understands and improves its content as a living marketplace: a dynamic system with feedback loops between users, creators/publishers, distribution, and long-term business outcomes.\n You will define a durable ML strategy that goes beyond “engagement metrics” to improve overall ecosystem health—identifying where we’re underserving content, uncovering the attributes that make content succeed, and designing optimization approaches that balance relevance, quality, diversity, integrity, and monetization. The problems are inherently multi-objective and long-horizon: the best decisions today should strengthen the ecosystem tomorrow. If you’re excited by high-leverage technical leadership, rigorous ML thinking, and marketplace-style dynamics at scale, this role offers a chance to directly shape Pinterest’s moat and the experience millions of people come to for ideas they can act on.\n What you’ll do: \n \n Set technical strategy and vision for ML systems that improve the end-to-end content ecosystem, including supply, distribution, and engagement/utility outcomes.\n Partner with DS teams to develop a content ecosystem measurement framework to quantify content health and performance (e.g., content quality, freshness, diversity, coverage, creator/content sustainability, and user value), and align it with company/business goals.\n Identify and close content gaps by building models and insights that answer: what content is missing, for whom, in which contexts, and why.\n Deeply understand what content works and why by combining causal thinking, experimentation, and model interpretability to connect content attributes and distribution mechanisms to downstream user and business outcomes.\n Build and optimize content marketplace mechanisms that balance multi-sided incentives and constraints (e.g., users, creators/publishers, advertisers, internal policy/safety), while maximizing long-term ecosystem value.\n Design multi-objective optimization approaches that manage tradeoffs across relevance, quality, diversity, creator incentives, integrity/safety, and monetization.\n Partner closely with cross-functional teams (Product, Data Science, UX Research, Content/Creator teams, Trust \u0026 Safety, Ads, Infra) to translate ambiguous ecosystem problems into clear technical roadmaps and deliver measurable impact.\n Mentor and grow junior ML engineers through technical coaching, design reviews, career development support, and creating a culture of strong engineering and scientific rigor.\n Raise the quality bar for ML engineering by establishing best practices for data quality, model governance, reliability, privacy-aware design, and operational excellence.\n Communicate clearly and influence broadly by producing crisp technical proposals, aligning stakeholders on tradeoffs, and driving decisions across org boundaries.\n Explore and apply advanced methods where beneficial—e.g., game-theoretic approaches, reinforcement learning, mechanism design, or bandit-style optimization—to improve marketplace dynamics and long-term ecosystem outcomes.\n \n What we’re looking for: \n \n Strong fundamentals in machine learning and optimization, with the ability to apply them to real-world, high-scale ecosystem problems.\n Demonstrated ability to lead technical strategy, navigate ambiguity, and deliver end-to-end impact.\n Deep interest in marketplace dynamics (multi-sided incentives, feedback loops, long-term health metrics), and comfort with multi-objective tradeoffs.\n Experience with Cursor, Copilot, Codex, or","salary_min":227871,"salary_max":469147,"location":"San Francisco, CA","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","code-generation","reinforcement-learning","llm","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=7919043","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T21:44:36Z","expires_at":"2026-06-29T14:08:27.825754Z","created_at":"2026-05-27T14:08:41.489689Z","updated_at":"2026-05-30T14:08:27.939307Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d134135d-62d9-4aa9-acb7-410bbd77911c"},{"id":"1e411b32-416c-4234-bcb3-3604b204f141","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Staff Machine Learning Engineer, AV Core","slug":"staff-machine-learning-engineer-av-core-1f2ae697","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 a Staff Machine Learning Engineer on Wayve’s Core Model Safety team in AV Core, you will help shape what our end-to-end driving model must understand to be safe and reliable in the real world - and turn that into trained capabilities, clear evidence, and adoption on the shared backbone across core and product engineering.\n  \n The Core Model Safety team builds foundational capabilities for assisted and automated driving - collision avoidance, scene understanding, model understanding, and robustness under failure. You will work in a focused, high-impact senior team with strong ownership, access to large-scale training and fleet data, and close partners in research, simulation, evaluation, and applied engineering.\n  \n Key responsibilities \n \n Drive Core Model Safety roadmap themes owning the full lifecycle from research to offline/online experiments to technology transfer.\n Train and deploy end-to-end AV 2.0 models on our global fleet, using large-scale, diverse data to validate capabilities and improve generalisation across vehicles, markets, and driving conditions.\n Build high-value open-loop and closed-loop evaluations for core capabilities and representation learning.\n Align priorities and learn from the organisation - with AV Core, Evaluation, and Product Engineering on roadmaps and failure modes; from fleet, simulation, and product feedback; and through mentoring others on the team.\n Maintain awareness of the wider business context - division and company priorities, near-term product programmes, and how Core Model Safety work enables them.\n \n About you   \n In order to set you up for success as a Staff Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.  \n  \n Essential  \n \n 5+ years in ML engineering, including pathfinding in ambiguous problems - from scoping and evals to establishing a direction (and knowledge transfer) for others to build on.\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 Hands-on experience with transformer-based and multimodal architectures, including vision-language models (VLM), vision-language-action models (VLA), or equivalent.\n Hands-on experience training shared representations with multiple tasks or objectives (multi-stage or joint training), including real trade-offs across data and losses.\n Staff-level technical leadership: research-literate and pragmatic, setting direction, raising the bar, and leading cross-functional work without formal line management.\n \n  \n Desirable  \n \n Prior experience in autonomous vehicles or robotics with hands-on deployment and closed-loop validation on physical systems.\n Experience in 3D scene understanding and representation learning for geometric and semantic perception, large-scale semantic enrichments.\n Experience in reward modelling, behaviour modelling, model introspection, and/or interpretability.\n Experience with redundant or fallback architectures, safety-critical systems.\n Experience across foundations/pretraining and applied engineering teams; large-scale training infrastructure and/or agentic workflows.\n \n This is a full-time role based in our office in Sunnyvale.  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. The reasonably estimated salary for this role ranges from $336,400 to $370,300, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.\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 no","salary_min":336400,"salary_max":370300,"location":"Sunnyvale, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["generative-ai","autonomous-vehicles","agents","robotics","pytorch","reinforcement-learning","gpu","pre-training"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8562545002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T19:02:03Z","expires_at":"2026-06-29T14:12:48.484991Z","created_at":"2026-05-27T14:13:12.451192Z","updated_at":"2026-05-30T14:12:48.600908Z","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/1e411b32-416c-4234-bcb3-3604b204f141"},{"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":"12749446-611e-48f3-b643-568d3c7be3f0","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Staff Product Manager - Infrastructure, Data \u0026 Security ","slug":"staff-product-manager-infrastructure-data-security-60ff4f34","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes between 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n The Role: \n We are looking for a Technical Product Manager to own the roadmap and execution across Snorkel AI's Infrastructure organization, spanning three engineering pods: Core Services, Developer Experience, and Security. This is not a customer-facing product role, you will be the PM for internal platforms, data infrastructure, and security systems that the rest of engineering and the business depend on. You will be the single point of coordination across a broad surface area that currently lacks dedicated product leadership.\n What You Will Do: \n The Infrastructure org owns the data platform, event systems, observability, developer tooling, release pipelines, and the full security stack. The engineering leadership and pod leads are very technical but the org needs a PM who can translate business and compliance requirements into prioritized infrastructure investments, manage dependencies across pods and partner teams, and hold the line on scope and delivery timelines. Today, prioritization decisions are made ad hoc by engineering leads who are also deep in technical execution. This role gives the org a dedicated owner for roadmap clarity, stakeholder alignment, and cross-pod coordination.\n Business context and infrastructure strategy: \n \n Develop a deep understanding of Snorkel AI's business, how the datasets are sold, and how should the platform be deployed, and scaled to support customers across AI labs, enterprise and federal and use that context to work closely with engineering leaders and product PMs to translate business scaling, security and functional needs into deployment patterns, and go-to-market requirements into concrete, prioritized projects for the Infrastructure org. Without this, infrastructure roadmaps become technically interesting but disconnected from what actually moves the business forward.\n \n Roadmap and prioritization across all three pods: \n \n Work with engineering leads to define quarterly roadmaps for Core Services (data platform, metrics platform, event systems, observability, fraud detection, infrastructure cost management, platform customization infrastructure, SDS project infrastructure), Developer Experience (CI/CD, release pipelines, dev systems, AI dev tooling), and Security (Auth0 migration, AuthN/AuthZ, cloud security, encryption).\n Make trade-off decisions when pods compete for shared resources or when new asks land mid-quarter. The org carries significant surface area relative to headcount. Your job is to ensure the team is working on the highest-leverage problems, not just the loudest requests.\n \n Stakeholder management and requirements gathering: \n \n Be the interface between the Infrastructure org and its consumers: product engineering teams who depend on Dataset APIs, Platform APIs, and Packaging APIs; the data science and analytics teams who depend on Cascade and the transformation layer; GTM and customer success teams who surface compliance and security requirements from enterprise and federal customers.\n Translate customer compliance requirements (SOC 2, FedRAMP, GDPR) into concrete security and data governance work items with clear acceptance criteria.\n \n Adoption and internal evangelism:  \n \n Proactively engage product PMs and engineering leads to ensure teams are leveraging infrastructure services, SDKs, and shared libraries (unified data access, event bus, security SDKs, CI/CD tooling) rather than building one-off solutions.\n Track adoption metrics, SDK integration coverage, service onboarding rates, migration completions, and identify teams that are lagging or working around infrastructure offerings. Close the loop by either driving adoption or feeding unmet requirements back into the roadmap.\n The Infrastructure org's impact is only as real as its adoption. You own the push to make sure the work doesn't just ship, it lands.\n \n Cross-pod coordination on foundational initiatives: \n \n Drive initiatives that span multiple pods, such as the unified data access library (which touches Core Services for the library itself, Security for auth/RBAC/encryption enforcement, and Developer Experience for SDK distribution and testing tooling).\n Coordina","salary_min":240000,"salary_max":300000,"location":"Redwood City, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["llm","reinforcement-learning","generative-ai","mlops","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6001496004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-20T18:27:52Z","expires_at":"2026-06-29T14:03:06.795706Z","created_at":"2026-05-27T14:03:19.232673Z","updated_at":"2026-05-30T14:03:06.906572Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/12749446-611e-48f3-b643-568d3c7be3f0"},{"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":591,"total_pages":30}