{"has_next":true,"jobs":[{"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":"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":"805ed157-8bdf-4b5b-91cb-a8a94d8c0226","company_id":"a0000000-0000-0000-0000-000000000003","title":"Technical Lead Manager, Physical AI","slug":"technical-lead-manager-physical-ai-13b9c024","description":"Scale AI is the data engine for the entire AI industry. Our mission is to accelerate the development of AI applications by providing organizations with the high-quality data they need. The Physical AI team at Scale is focused on the next frontier: building general AI that can reason and act in the physical world. By leveraging Scale’s massive data infrastructure, we are helping frontier labs build Foundation Models for Physical AI that will redefine the future of automation.\n Role Overview \n As the Technical Lead Manager (TLM) for the Physical AI team of Scale , you will bridge the gap between cutting-edge Machine Learning research and physical robot deployment. You will lead a high-performing team of Research Engineers while remaining a hands-on technical contributor (~60% of your time).\n Your primary focus will be the development and evaluation of Large-Scale Foundation Models (e.g VLAs, World models) that allow robots and AVs to generalize across diverse tasks, environments, and morphologies.\n Key Responsibilities \n Technical Leadership \u0026 Research \n \n Model Scaling: Direct research into scaling laws for Physical AI, determining how to best utilize massive datasets for pre-training and fine-tuning generalist policies.\n VLA and World model development: Develop novel methods for developing and evaluating models, including new Physical AI industry benchmarks\n Hands-on Modeling: Actively write code to implement, train and test SOTA architectures. Conduct research on Physical AI data collection, cross-embodiment training, and policy fine-tuning.  \n Data Strategy: Collaborate with internal labeling teams to design \"robotic-native\" data pipelines, including the use of VLMs for automated trajectory annotation and data synthesis.\n Collaborate closely with customers to drive the industry forward in using Scale data \n \n Team Management \u0026 Execution \n \n Mentorship: Lead and grow a team of 4-6 elite Physical AI  researchers, fostering a culture of high-velocity experimentation and rigorous evaluation.\n Paper-to-Product: Translate the latest research from NeurIPS, ICRA, and CVPR into production-ready features for Scale’s Physical AI partners.\n Cross-functional Alignment: Work with cross-functional teams (e.g Product and Operations) to bring our research breakthroughs into production. \n \n Required Qualifications \n AI/ML Excellence \n \n Deep Learning Mastery: Expert-level proficiency in PyTorch , with deep knowledge of Transformer architectures , Attention mechanisms , and Self-Supervised Learning .\n VLM/VLA Experience: Proven track record of working with Vision-Language Models (e.g., CLIP, PaLM-E) and adapting them for spatial reasoning or embodied tasks.\n Generative AI: Experience with Diffusion Models for sequence generation or Generative World Models for predictive modeling.\n \n Physical AI \u0026 Software Background \n \n Embodied AI: Strong understanding of Physical AI stack, including imitation learning, reinforcement learning (RL), and multi-modal sensor fusion.\n Infrastructure: Experience with large-scale distributed training across GPU clusters and high-performance data loading.\n Leadership: 1+ years of experience leading technical teams or projects in a research-intensive environment.\n \n Nice to Haves: \n \n Publication Record: First-author publications at top-tier AI/ML conferences (NeurIPS, CVPR, ICRA, CoRL).\n Hardware Generalization: Experience building models that work across different robot types (arms, mobile bases, humanoids).\n Sim-to-Real: Experience with high-fidelity simulators (e.g., Isaac Gym, MuJoCo) and the nuances of physical domain adaptation.\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n The base salary range for this full-time position in the location of San Francisco is:\n $248,800 — $311,000 USD \n PLEASE NOTE:  Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. \n About Us: \n ","salary_min":248800,"salary_max":311000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["pre-training","deep-learning","gpu","diffusion-models","search","data-pipeline","pytorch","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4693453005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-08T16:16:36Z","expires_at":"2026-06-29T14:01:17.804097Z","created_at":"2026-05-10T14:01:23.558094Z","updated_at":"2026-05-30T14:01:17.913973Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/805ed157-8bdf-4b5b-91cb-a8a94d8c0226"},{"id":"6cc4f021-a9ac-47ce-8f68-956479b4a3e0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Operations, External Artifacts","slug":"research-operations-external-artifacts-f2170026","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 Every time Anthropic releases a model, we publish a system card: a long-form technical document that describes the model's capabilities, safety properties, evaluation results, and the reasoning behind our deployment decisions. System cards are some of the most consequential and widely read documents we produce, and they are one way we hold ourselves publicly accountable for the safety claims we make.\n We're hiring a Research Operations Specialist to help own system card production. You'll work embedded with research and safety teams through each launch, coordinating contributions from dozens of researchers, holding the schedule and the open-threads list, and making sure the document ships on time as a single, accurate, internally consistent whole. Along the way you'll do real editorial work: turning results and researcher notes into clear, honest prose and pushing back when an explanation doesn't hold together.\n System cards sit within a wider family of external safety artifacts, including risk reports and Responsible Scaling Policy updates. Part of this role is keeping the system card consistent with those documents so that Anthropic's public safety story reads as one coherent account rather than several.\n This role sits in Research Operations and works closely with Alignment, Safeguards, Frontier Red Team, and capabilities research. The core of the job is part project management, part translation: keeping a complex, many-author, hard-deadline document on track while making frontier safety research legible to researchers, policymakers, journalists, and the public — without sacrificing precision.\n Key responsibilities:\n \n Drive system card production end to end — own the timeline, the contributor list, the open-threads tracker, and the definition of done for each launch\n Coordinate dozens of contributors across Alignment, Safeguards, Frontier Red Team, Interpretability, and capabilities; chase drafts, resolve differences of perspective, seek ground truth, and run final document beautification\n Edit, and sometimes write, content; work directly with researchers to turn their results, notes, and plots into clear, scientific, non-marketing prose and maintain Anthropic's voice across sections drafted by many different people\n Guard accuracy and consistency; catch terminology drift, claims that subtly contradict each other, and discrepancies between internal communications and system card drafts\n Keep the system card aligned with related artifacts; track what's being said in risk reports, RSP disclosures, and other safety documentation so the system card stays consistent with them, and flag conflicts early\n Improve the process between launches; build and maintain templates, style guidance, contributor guides, checklists, and reusable section scaffolds so each cycle starts from a stronger baseline\n Pick up other research-adjacent operations and writing, such as internal research summaries, release notes, and documentation that helps research leadership communicate clearly\n \n Minimum qualifications:\n \n Strong project management and execution skills; able to track dozens of open threads to closure during compressed launch periods with many moving parts and many owners\n Demonstrated ability to coordinate and influence without direct authority, building trust while chasing drafts and giving constructive editorial feedback\n Excellent technical writing skills; able to take dense, jargon-heavy source material and produce prose that is precise, honest, and readable by a smart non-specialist\n Comfort working closely with researchers; able to read an evaluation results table, ask the right clarifying questions, and push back when an explanation doesn't hold together\n Working knowledge of large language models at a conceptual level, including fluency with vocabulary such as pretraining, RLHF, context windows, evals, and red-teaming\n Data communication literacy; able to identify how a chart or table could be made clearer and more accurate\n High integrity and a genuine sense of accountability around producing documents that hold Anthropic publicly responsible for its safety claims\n \n Preferred qualifications:\n \n Familiarity with AI safety, AI policy, alignment research, evaluation methodology, or the RSP landscape beyond baseline LLM knowledge\n Background in science communication, research publishing, or technical journalism\n A track record of shipping long-form technical documents: research reports, whitepapers, technical standards, regulatory filings, or science journalism\n Experience with safety, risk, or compliance documentation such as regulatory submissions, safety","salary_min":260000,"salary_max":310000,"location":"Remote (US)","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","pre-training","alignment","llm","healthcare","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5208278008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-05T17:50:48Z","expires_at":"2026-06-29T14:00:23.126762Z","created_at":"2026-05-06T14:00:32.514787Z","updated_at":"2026-05-30T14:00:23.237695Z","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/6cc4f021-a9ac-47ce-8f68-956479b4a3e0"},{"id":"ded9ce3f-76f4-4c51-8018-38d4f97c474a","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Machine Learning (RL Velocity)","slug":"research-engineer-machine-learning-rl-velocity-c61986b1","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 The RL Velocity team owns the efficiency and reliability of our RL Science stack - the infrastructure, tooling, and systems that let researchers iterate quickly on training runs. As a Research Engineer on the team, you'll build and improve the core platform that underpins how we do RL at Anthropic, removing bottlenecks that slow down research and making it easier for the broader org to ship better models faster. This is high-leverage work: small improvements to velocity compound across every researcher and every run.\n Responsibilities \n \n Build and improve the RL training infrastructure that researchers depend on day-to-day\n Identify and remove bottlenecks across the RL stack: debugging, profiling, and rearchitecting where needed\n Partner closely with researchers and with adjacent engineering teams (inference, sandboxing, and many more) to understand pain points and ship tooling that makes them faster\n Own the reliability and performance of research runs end-to-end\n Contribute to design decisions that shape how Anthropic does RL at scale\n \n You may be a good fit if you \n \n Have strong software engineering fundamentals and a track record of building performant, reliable systems\n Have worked on ML infrastructure, distributed systems, or research tooling\n Care about enabling other people's work and find leverage through platforms rather than individual experiments\n Are comfortable operating across the stack, from low-level performance work to RL algorithms\n Have a bias toward shipping and iterating quickly, with a mix of high agency and low ego\n \n Strong candidates may also have \n \n Experience with large-scale distributed training (RL, pre-training, or post-training)\n Familiarity with JAX, PyTorch, or similar ML frameworks\n A track record of operating at the edge of research and infra in a fast-moving environment\n \n Deadline to apply: None. Applications will be reviewed on a rolling basis.\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 $500,000 — $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 communication, don't click any links—visit  anthropic.com/careers  directly for confirmed position openings.\n How we're different \n We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzz","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"principal","tags":["alignment","search","distributed-systems","pre-training","pytorch","machine-learning","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5198108008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-23T14:35:36Z","expires_at":"2026-06-29T14:00:21.361302Z","created_at":"2026-04-30T05:46:34.053245Z","updated_at":"2026-05-30T14:00:21.472529Z","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/ded9ce3f-76f4-4c51-8018-38d4f97c474a"},{"id":"e166cabe-5ba5-4fe5-a30d-688ddd5f8fc1","company_id":"5dfcd8fc-f8dd-4f46-b613-ca6da467ff4b","title":"Machine Learning Researcher, Audio","slug":"machine-learning-researcher-audio-6b0906fa","description":"MACHINE LEARNING RESEARCHER, AUDIO\n\nLocation: San Francisco, CA or Remote (US)\n\n \n \n\n\nABOUT BLAND\n\nAt Bland.com, our mission is to empower enterprises to build AI phone agents at scale. Based in San Francisco, we are a fast-growing team reimagining how customers interact with businesses through voice. We have raised $65 million from leading Silicon Valley investors, including Emergence Capital, Scale Venture Partners, Y Combinator, and founders of Twilio, Affirm, and ElevenLabs.\n\n \n\nVoice is quickly becoming the primary interface between businesses and their customers. We are building the models and infrastructure that make those interactions feel natural, reliable, and genuinely human.\n\n \n \n\n\nTHE ROLE: MACHINE LEARNING RESEARCHER, AUDIO\n\nAs a Machine Learning Researcher at Bland, you'll be working on foundational research and development across the core components of our voice stack: speech-to-text, large language models, neural audio codecs, and text-to-speech. Your work will define how our agents understand, reason, and speak in real time at enterprise scale.\n\n \n\nThis is not a narrow research role. You will take ideas from theory to large-scale training to production inference systems serving millions of calls per day. You will design new modeling approaches, validate them with rigorous experimentation, and collaborate with engineering teams to deploy them into real customer environments.\n\n \n \n\n\nWHAT YOU WILL DO\n\nBuild and Scale Next-Generation TTS Systems\n\n - Design and train large scale text-to-speech models capable of expressive, controllable, human-sounding output.\n\n - Develop neural audio codec-based TTS architectures for efficient, high-fidelity generation.\n\n - Improve prosody modeling, question inflection, emotional expression, and multi-speaker robustness.\n\n - Optimize for real-time, low-latency inference in production.\n\n \n\nAdvance Speech-to-Text Modeling\n\n - Build and fine-tune large scale ASR systems robust to accents, noise, telephony artifacts, and code switching.\n\n - Leverage self-supervised pretraining and large-scale weak supervision.\n\n - Improve transcription accuracy for real-world enterprise scenarios, including structured extraction and conversational nuance.\n\n \n\nPioneer Neural Audio Codecs\n\n - Research and implement neural audio codecs that achieve extreme compression with minimal perceptual loss.\n\n - Explore discrete and continuous latent representations for scalable speech modeling.\n\n - Design codec architectures that enable downstream generative modeling and controllable synthesis.\n\n \n\nDevelop Scalable Training Pipelines\n\n - Curate and process massive audio datasets across languages, speakers, and environments.\n\n - Design staged training curricula and data filtering strategies.\n\n - Scale training across distributed GPU clusters focusing on cost, throughput, and reliability.\n\n \n\nRun Rigorous Experiments\n\n - Design ablation studies that isolate the impact of architectural changes.\n\n - Measure improvements using both objective metrics and perceptual evaluations.\n\n - Validate ideas quickly through focused experiments that confirm or eliminate hypotheses.\n\n \n \n\n\nWHAT MAKES YOU A GREAT FIT\n\nDeep Research Foundations\n\n - Experience with self-supervised learning, multimodal modeling, or generative modeling.\n\n - Ability to derive new formulations and implement them efficiently.\n\n \n\nExpertise in Voice Modeling\n\n - Hands-on experience building or scaling TTS, STT, or neural audio codec systems.\n\n - Familiarity with large scale speech datasets and real-world audio variability.\n\n - Strong intuition for audio quality, prosody, and conversational dynamics.\n\n \n\nSystems and Hardware Awareness\n\n - Experience training and serving large models on modern accelerators.\n\n - Knowledge of inference optimization techniques, including quantization, kernel optimization, and memory efficiency.\n\n - Understanding of real-time constraints in telephony or streaming environments.\n\n \n\nExperimental Rigor\n\n - Track record of designing controlled experiments and meaningful ablations.\n\n - Comfortable working with both offline benchmarks and live production metrics.\n\n - Ability to move quickly from hypothesis to validation.\n\n \n\nBuilder Mentality\n\n - Comfortable in fast-moving startup environments.\n\n - Strong ownership mindset from research through deployment.\n\n - Excited by ambiguous, unsolved problems.\n\n \n \n\n\nHOW YOU SHOW UP\n\n - You treat unsolved problems as opportunities to invent new paradigms.\n\n - You identify the single experiment that can validate an idea in days, not months.\n\n - You measure everything and let data drive decisions.\n\n - You are obsessed with making voice agents sound truly human.\n\n - You use AI tools aggressively to amplify your own impact and accelerate research cycles.\n\n \n \n\n\nBONUS POINTS\n\n - Experience with large scale distributed training.\n\n - Research publications or open source contributions in speech or language AI.\n\n - Background in real-time speech systems or telephon","salary_min":160000,"salary_max":250000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["gpu","distributed-systems","healthcare","speech","llm","pre-training","machine-learning","research"],"apply_url":"https://jobs.ashbyhq.com/bland/2e815d0d-8e7a-43cc-8853-c1b029aeb499/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-20T22:07:11.702Z","expires_at":"2026-06-29T14:06:16.806176Z","created_at":"2026-04-22T15:40:14.708917Z","updated_at":"2026-05-30T14:06:16.920768Z","company_name":"Bland AI","company_slug":"bland-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=bland.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e166cabe-5ba5-4fe5-a30d-688ddd5f8fc1"},{"id":"92e0e6b0-3459-44ec-9e1a-4e36a7b805d4","company_id":"4d985fa4-b897-4f93-9745-c332367ad86b","title":"Research Scientist - LLM ","slug":"research-scientist-llm-80f40837","description":"ABOUT RETELL AI\n\nRetell AI is using first principles to reimagine the call center with cutting-edge voice AI.\n\nThousands of companies now utilize Retell’s AI voice agents to handle sales, support, and logistics calls that once required large teams of human agents. Backed by Y Combinator, Alt Capital, and other leading investors, we have scaled to $60M ARR with a team of 40 people, up from $5M at the start of 2025.\n\nOur vision for 2026 is to build a modern CX platform where entire contact centers are powered by AI. Instead of basic automation that needs constant human tuning, we’re creating intelligent AI “workers” that can act as frontline agents, QA analysts, and managers — continuously executing, monitoring, and improving customer interactions.\n\nWe’re growing quickly and looking for ambitious builders who want to tackle hard technical problems, move fast, and have real impact at one of the fastest-growing voice AI startups.\n\nLet’s build the future together.\n\n - We’re a top 50 AI app in a16z list: https://tinyurl.com/5853dt2x\n\n - #4 on Brex's Fast-Growing Software Vendors of 2025: https://www.brex.com/journal/brex-benchmark-december-2025\n\n - We're also one of the top ranking startups on: https://leanaileaderboard.com/\n\n - Enterprise tech 30: https://www.wing.vc/et30/overview\n\n\n\n\nABOUT THE ROLE\n\nThis is a research-driven, high-impact role for ML researchers who want to push the boundaries of real-time AI. As a Founding Machine Learning Research Engineer at Retell, you’ll focus on advancing model capabilities for human-like voice agents operating in complex, real-world environments.\n\nYou’ll explore new approaches across LLMs and audio models, design novel evaluation methods, and prototype systems that improve reasoning, latency, and conversational quality. Your work will directly influence production systems, bridging cutting-edge research with real-world deployment.\n\nIf you’re excited about solving open-ended ML problems, experimenting rapidly, and shaping how voice AI systems think and perform, this is a unique opportunity to do so at scale.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Research \u0026 Experimentation – Explore and develop new techniques across LLMs and audio models to improve reasoning, latency, and conversational quality in real-time systems.\n\n - Model Training – Rapidly build and iterate on models and pipelines, turning research ideas into working prototypes. Innovate on paradigms, training methods, and inference.\n\n - Evaluation \u0026 Benchmarking – Design novel evaluation frameworks, datasets, and metrics to measure performance on complex, real-world voice tasks.\n\n - Bridge Research to Production – Collaborate closely with engineering to translate research insights into deployable systems.\n\n - Human Feedback Loops – Develop methods to incorporate human evaluation into model improvement, especially for subjective conversational quality.\n\n - Advance the Frontier – Stay at the cutting edge of ML research and bring new ideas into Retell’s product and infrastructure.\n\n\n\n\nREQUIRED\n\n - Strong ML Research Background – You've worked on advanced ML problems (like LLM pre-training and post-training, transcription model training, TTS, or multimodal systems), either in industry or academia.\n\n - Deep Technical Foundation – Comfortable with PyTorch, model architectures, and the math behind modern machine learning.\n\n - Top Academic Background – Master's degree in CS, ML, AI or related field required; PhD preferred. Equivalent research-level engineering experience also considered.\n\n\n\n\nYOU MIGHT THRIVE IF YOU\n\n - Published or Awarded – First/co-author publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, Interspeech, etc.) or notable competition awards are a strong plus.\n\n - Experimental Mindset – You enjoy exploring open-ended problems and iterating quickly on ideas.\n\n - Bridge Theory \u0026 Practice – You can translate research into systems that work in real-world environments.\n\n - Startup-Ready – You thrive in fast-paced environments with high ownership and ambiguity.\n\n - Collaborative \u0026 Clear Communicator – You can explain complex ideas and work cross-functionally to drive impact.\n\n\n\n\nJOB DETAILS\n\n - Cash: $225,000 - $400,000 base salary\n\n - Equity: Offers Equity\n\n - Location: Redwood City, CA, US (100% Relocation Provided)\n\n - US Visas: Retell AI is open to sponsoring work authorization for qualified candidates, including H1B/H-1B, TN, L-1, E-3, F-1 (OPT/CPT), and O-1 visas.\n\n\n\n\nOTHER BENEFITS\n\n - 100% coverage for medical, dental, and vision insurance\n\n - $70/day DoorDash credit for unlimited meals and snacks\n\n - $200/month wellness reimbursement\n\n - $300/month commuter reimbursement\n\n - $75/month phone bill reimbursement\n\n - $50/month internet reimbursement\n   \n   \n\n\nCOMPENSATION PHILOSOPHY\n\n - Best Offer Upfront: Choose from three cash-equity balance options, no negotiation needed\n\n - Top 1% Talent: Above-market pay (top 5 percentile)\n\n - High Ownership: Small teams, \u003e$","salary_min":225000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["llm","pytorch","search","speech","pre-training","research"],"apply_url":"https://jobs.ashbyhq.com/retell-ai/b0d780eb-df25-49d0-859a-915de204a2f2/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-14T05:52:56.477Z","expires_at":"2026-06-29T14:11:20.587907Z","created_at":"2026-04-16T11:17:45.913083Z","updated_at":"2026-05-30T14:11:20.696475Z","company_name":"Retell AI","company_slug":"retell-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=retellai.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/92e0e6b0-3459-44ec-9e1a-4e36a7b805d4"},{"id":"acc5d396-6aa2-40ba-8a49-632774606bde","company_id":"4d985fa4-b897-4f93-9745-c332367ad86b","title":"Research Scientist - Audio ","slug":"research-scientist-audio-918408c6","description":"ABOUT RETELL AI\n\nRetell AI is using first principles to reimagine the call center with cutting-edge voice AI.\n\nThousands of companies now utilize Retell’s AI voice agents to handle sales, support, and logistics calls that once required large teams of human agents. Backed by Y Combinator, Alt Capital, and other leading investors, we have scaled to $60M ARR with a team of 40 people, up from $5M at the start of 2025.\n\nOur vision for 2026 is to build a modern CX platform where entire contact centers are powered by AI. Instead of basic automation that needs constant human tuning, we’re creating intelligent AI “workers” that can act as frontline agents, QA analysts, and managers — continuously executing, monitoring, and improving customer interactions.\n\nWe’re growing quickly and looking for ambitious builders who want to tackle hard technical problems, move fast, and have real impact at one of the fastest-growing voice AI startups.\n\nLet’s build the future together.\n\n - We’re a top 50 AI app in a16z list: https://tinyurl.com/5853dt2x\n\n - #4 on Brex's Fast-Growing Software Vendors of 2025: https://www.brex.com/journal/brex-benchmark-december-2025\n\n - We're also one of the top ranking startups on: https://leanaileaderboard.com/\n\n - Enterprise tech 30: https://www.wing.vc/et30/overview\n\n\n\n\nABOUT THE ROLE\n\nThis is a research-driven, high-impact role for ML researchers who want to push the boundaries of real-time AI. As a Founding Machine Learning Research Engineer at Retell, you’ll focus on advancing model capabilities for human-like voice agents operating in complex, real-world environments.\n\nYou’ll explore new approaches across LLMs and audio models, design novel evaluation methods, and prototype systems that improve reasoning, latency, and conversational quality. Your work will directly influence production systems, bridging cutting-edge research with real-world deployment.\n\nIf you’re excited about solving open-ended ML problems, experimenting rapidly, and shaping how voice AI systems think and perform, this is a unique opportunity to do so at scale.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Research \u0026 Experimentation – Explore and develop new techniques across LLMs and audio models to improve reasoning, latency, and conversational quality in real-time systems.\n\n - Model Training – Rapidly build and iterate on models and pipelines, turning research ideas into working prototypes. Innovate on paradigms, training methods, and inference.\n\n - Evaluation \u0026 Benchmarking – Design novel evaluation frameworks, datasets, and metrics to measure performance on complex, real-world voice tasks.\n\n - Bridge Research to Production – Collaborate closely with engineering to translate research insights into deployable systems.\n\n - Human Feedback Loops – Develop methods to incorporate human evaluation into model improvement, especially for subjective conversational quality.\n\n - Advance the Frontier – Stay at the cutting edge of ML research and bring new ideas into Retell’s product and infrastructure.\n\n\n\n\nREQUIRED\n\n - Strong ML Research Background – You've worked on advanced ML problems (like LLM pre-training and post-training, transcription model training, TTS, or multimodal systems), either in industry or academia.\n\n - Deep Technical Foundation – Comfortable with PyTorch, model architectures, and the math behind modern machine learning.\n\n - Top Academic Background – Master's degree in CS, ML, AI or related field required; PhD preferred. Equivalent research-level engineering experience also considered.\n\n \n\n\nYOU MIGHT THRIVE IF YOU\n\n - Published or Awarded – First/co-author publications at top-tier venues (NeurIPS, ICML, ICLR, ACL, Interspeech, etc.) or notable competition awards are a strong plus.\n\n - Experimental Mindset – You enjoy exploring open-ended problems and iterating quickly on ideas.\n\n - Bridge Theory \u0026 Practice – You can translate research into systems that work in real-world environments.\n\n - Startup-Ready – You thrive in fast-paced environments with high ownership and ambiguity.\n\n - Collaborative \u0026 Clear Communicator – You can explain complex ideas and work cross-functionally to drive impact.\n\n\n\n\nJOB DETAILS\n\n - Cash: $225,000 - $400,000 base salary\n\n - Equity: Offers Equity\n\n - Location: Redwood City, CA, US (100% Relocation Provided)\n\n - US Visas: Retell AI is open to sponsoring work authorization for qualified candidates, including H1B/H-1B, TN, L-1, E-3, F-1 (OPT/CPT), and O-1 visas.\n\n\n\n\nOTHER BENEFITS\n\n - 100% coverage for medical, dental, and vision insurance\n\n - $70/day DoorDash credit for unlimited meals and snacks\n\n - $200/month wellness reimbursement\n\n - $300/month commuter reimbursement\n\n - $75/month phone bill reimbursement\n\n - $50/month internet reimbursement\n   \n   \n\n\nCOMPENSATION PHILOSOPHY\n\n - Best Offer Upfront: Choose from three cash-equity balance options, no negotiation needed\n\n - Top 1% Talent: Above-market pay (top 5 percentile)\n\n - High Ownership: Small teams, ","salary_min":225000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["pre-training","pytorch","speech","search","llm","research"],"apply_url":"https://jobs.ashbyhq.com/retell-ai/7dbe5404-e08c-4c62-99dc-ef050534d029/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-14T05:52:52.3Z","expires_at":"2026-06-29T14:11:20.507014Z","created_at":"2026-04-16T11:17:45.838238Z","updated_at":"2026-05-30T14:11:20.622831Z","company_name":"Retell AI","company_slug":"retell-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=retellai.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/acc5d396-6aa2-40ba-8a49-632774606bde"},{"id":"fa6ff3fa-42a6-4cf3-bf3f-cb02272b2b6b","company_id":"e5e49ca2-fa01-4747-a951-326be14de524","title":"Forward Deployed Research Scientist","slug":"forward-deployed-research-scientist-1d72832e","description":"Shape the Future of AI \n At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially.\n About Labelbox \n We're the only company offering three integrated solutions for frontier AI development:\n \n Enterprise Platform \u0026 Tools : Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale\n Frontier Data Labeling Service : Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models\n Expert Marketplace : Connecting AI teams with highly skilled annotators and domain experts for flexible scaling\n \n Why Join Us \n \n High-Impact Environment : We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions.\n Technical Excellence : Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence.\n Innovation at Speed : We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution.\n Continuous Growth : Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI.\n Clear Ownership : You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics.\n \n Role Overview \n Alignerr is Labelbox's human data organization — we produce the training data that frontier AI labs use to build their most capable models. Our Forward Deployed Research Team sits at the intersection of research science and client delivery, embedding research capability directly into the engagements that drive our business.\n This is not a traditional research scientist role. You will not spend months pursuing a single research question. You will work on multiple client engagements simultaneously, operating on timescales of days to weeks. You will sit in scoping meetings with research teams at major AI labs, reason scientifically about data strategy in real time, fine-tune open-weight models to validate our data methodology, and collaborate with our Applied Research team to turn client-grounded findings into published work. The pace is fast, the problems are applied, and the feedback loops are short.\n We are looking for someone who finds that energizing, not compromising.\n Your Impact \n \n \n Engage directly with frontier lab research teams. You will be in the room during client scoping meetings — not as support staff, but as a technical peer. You'll engage on methodology, challenge assumptions about data requirements, and shape project specifications based on a scientific understanding of how data composition affects model outcomes.\n Develop deep scientific understanding of client engagements. For each project, you will build a working model of the client's architecture, training methodology, and target capabilities. You'll use this understanding to reason about why a particular data strategy will or won't work, identify risks early, and iterate with empirical grounding — not intuition.\n Run ablation studies and fine-tune open-weight models. You will fine-tune models on client data (and proxy data) to empirically measure the impact of our data on model performance. This is how we validate that what we deliver actually improves our customers' models — and how we catch problems before the client does.\n Consult on workflow and quality systems. You will partner with our Human Data Operations team to review annotation schemas, task designs, and quality rubrics before projects go into execution. Your job is to ensure the spec is technically sound — that the data we produce will actually serve the client's training objectives.\n Collaborate with Applied Research on publications and benchmarks. Our Applied Research team owns the long-horizon research agenda. Your role is to feed them signal from the field — generalizable findings, reusable methodologies, empirical results — and help drive joint projects to completion. You will contribute to benchmarks, white papers, and conference submissions that establish Labelbox's research credibility.\n \n What You Bring \n \n \n Required \n \n MS or PhD in Machine Learning, NLP, Computer Science, or a related quantitative field.\n Hands-on experience fine-tuning large language models (open-weight models such as Llama, Mistral, Qwen, or similar).\n Strong understanding of LLM training pipelines — pretraining, supervised fine-tuning, RLHF/DPO, and how data quality and composition affect each stage.\n Experience designing and executing experiments with r","salary_min":200000,"salary_max":300000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","llm","reinforcement-learning","data-pipeline","nlp","pre-training","research"],"apply_url":"https://job-boards.greenhouse.io/labelbox/jobs/5101375007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-13T23:17:35Z","expires_at":"2026-06-29T14:04:43.087758Z","created_at":"2026-04-14T01:28:51.420317Z","updated_at":"2026-05-30T14:04:43.199469Z","company_name":"Labelbox","company_slug":"labelbox","company_logo_url":"https://www.google.com/s2/favicons?domain=labelbox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fa6ff3fa-42a6-4cf3-bf3f-cb02272b2b6b"},{"id":"63cf43b2-24aa-49be-a1a0-8e7071a975af","company_id":"e452e377-b504-47ba-85cc-b47aa09c3067","title":"AI/ML Scientist, Protein Foundation Models","slug":"aiml-scientist-protein-foundation-models-ccdf307f","description":"Manifold Bio is a platform biotechnology company pioneering AI-guided protein design and massively multiplexed in vivo screening to unlock tissue-targeted medicines and organism-scale models of living systems. Using proprietary molecular barcoding technology, we screen hundreds of thousands of protein designs simultaneously in living systems, producing in vivo-validated datasets at a scale no one else can match. The datasets power our computational models, which leads to better drug designs, creating a flywheel that gets stronger with every campaign. Our team of protein engineers, biologists, and computational scientists works  across this full stack to pursue programs both internally and with leading pharma companies. \n Position \n Manifold's AI team is actively training protein foundation models on our proprietary experimental datasets. Our generative antibody design model, mBER, has already demonstrated controllable de novo binder design across multiple million-scale screening campaigns, and the team is now scaling foundation model capabilities to push well beyond current performance. We are looking for an AI/ML Scientist to join this effort. You will work alongside our existing model training team to accelerate the development of foundation models fine-tuned on Manifold's data, bringing additional depth in pre-training methodology, architecture development, and large-scale training. Your work will directly improve mBER's design capabilities and unlock new modeling paradigms for the broader team. You'll own foundation model projects end-to-end, from architecture selection and training infrastructure to evaluation against real experimental outcomes, while contributing to the team's shared research agenda.\n This is an on-site role and can be based in either Boston, Massachusetts or San Francisco, California. Please only apply if you reside in these cities or are open to relocate.  \n Responsibilities \n \n Advance the team's ongoing foundation model training efforts—pretraining, fine-tuning, and evaluating folding, docking, language, and generative design models on Manifold's proprietary experimental data\n Bring depth in training methodology, architecture selection, and optimization to complement the existing team's expertise\n Develop and scale training pipelines for distributed, multi-GPU and multi-node training runs\n Integrate foundation model outputs into mBER to improve binder design success rates and enable new design capabilities\n Design and execute ML experiments with clear hypotheses, rigorous evaluation frameworks, and systematic analysis\n Establish best practices for mixed-precision training, gradient checkpointing, and computational efficiency at scale\n Produce clear documentation and analysis supporting architecture and training decisions\n \n Required Qualifications \n \n Demonstrated experience pretraining and/or fine-tuning protein foundation models (folding, docking, language models, or generative design) with published or otherwise demonstrable results\n Strong familiarity with AlphaFold architecture and training methodology\n 2+ years of hands-on experience with PyTorch and/or JAX for deep learning\n Experience with large-scale model training: distributed training, multi-GPU/multi-node setups, mixed precision, gradient checkpointing\n Solid understanding of deep learning architectures (transformers, attention mechanisms, diffusion/flow matching) and optimization techniques\n Experience working with protein structure data (PDB, mmCIF) and/or protein sequence datasets\n Strong statistical analysis and experimental design skills\n Proficiency in Python scientific computing stack (NumPy, Pandas, scikit-learn)\n Self-directed researcher who can balance guidance with independence\n Excellent written and verbal communication skills for cross-functional collaboration\n \n Preferred Qualifications \n \n Experience with protein generative design methods (e.g., RFdiffusion, ProteinMPNN, flow matching approaches)\n Experience with protein language models (e.g., ESM family)\n Published research in computational biology, protein design, or structural biology\n Experience training on proprietary or domain-specific biological datasets\n Familiarity with Ray for distributed computing\n Experience with Kubernetes (EKS) and cloud computing platforms (AWS)\n Knowledge of protein engineering, directed evolution, or structural biology wet lab techniques\n Experience working with agentic AI coding tools for fast, parallelized execution of modeling experiments\n Previous biotech/pharma industry experience\n \n This Role Might Be Perfect For You If: \n \n You have deep experience training protein foundation models and want to apply that expertise to some of the richest proprietary experimental datasets in the field\n You're excited about pushing beyond public model performance by leveraging unique, large-scale in vivo screening data\n You thrive in high-ownership roles where you can drive research direction while collaborating with a tight-kni","salary_min":140000,"salary_max":225000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"junior","tags":["generative-ai","fine-tuning","pytorch","distributed-systems","jax","pre-training","agents","deep-learning"],"apply_url":"https://job-boards.greenhouse.io/manifoldbio/jobs/5106955007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-04-13T21:35:41Z","expires_at":"2026-06-29T14:14:11.250881Z","created_at":"2026-04-16T18:53:13.456204Z","updated_at":"2026-05-30T14:14:11.371328Z","company_name":"Manifold Bio","company_slug":"manifold-bio","company_logo_url":"https://www.google.com/s2/favicons?domain=manifoldbio.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/63cf43b2-24aa-49be-a1a0-8e7071a975af"},{"id":"4967c4a2-2640-422f-bb55-49803f326195","company_id":"31ae48bc-c938-4c26-a348-0bf3c089a446","title":"VP of Product, Research and Training Infrastructure","slug":"sr-director-of-product-research-and-training-infrastructure-433c0257","description":"CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at  www.coreweave.com . \n The Mission \n As CoreWeave continues to solidify its position as the Essential Cloud for AI , we are seeking a visionary VP of Product , Research Training Infrastructure . This executive leader will own the product strategy and engineering execution for the services that power the most ambitious AI research labs in the world. You will bridge the gap between \"the metal\" and the researcher, delivering a seamless, high-performance environment where frontier models are born.\n The Role: Architect of the AI Factory \n You will lead the product strategy of our Research Training Stack , focusing on the specialized orchestration, evaluation, and iteration tools required for massive-scale pre-training and post-training. This is a mission-critical role at the intersection of high-performance computing (HPC) and cloud-native agility.\n Core Responsibilities \n \n Frontier Orchestration: Oversee the evolution of SUNK (Slurm on Kubernetes) to provide researchers with deterministic, bare-metal performance through a cloud-native interface.\n Holistic Training Services: Beyond Slurm, drive the development of next-generation orchestrators and automated training-based evaluation frameworks that ensure model quality throughout the lifecycle.\n Post-Training Excellence: Build the infrastructure required for sophisticated Reinforcement Learning (RL) and RLHF pipelines, enabling labs to refine foundation models with maximum efficiency.\n Customer Advocacy: Act as the primary technical partner for lead researchers at global AI labs, translating their \"future-state\" requirements into actionable product roadmaps.\n \n Requirements: Deep Research \u0026 Infrastructure Mastery \n \n Proven Leadership: 15+ years of experience in engineering leadership, with at least 5+ years managing large-scale infrastructure at a top-tier research lab or an AI-native cloud provider.\n Domain Expertise: Deep, hands-on knowledge of Slurm , Kubernetes , and the specific networking requirements (InfiniBand/RDMA) for distributed training clusters.\n Research Mindset: You likely come from a background supporting frontier model research (pre-training and post-training) and understand the \"pain points\" of a research scientist.\n Scaling Experience: A track record of delivering mission-critical services on multi-thousand GPU clusters (H100/Blackwell/Rubin architectures).\n Strategic Vision: Ability to define \"what’s next\" in the AI stack, from automated RL loops to specialized sandbox environments.\n \n Why CoreWeave? \n In 2026, CoreWeave is the foundation of the largest infrastructure buildout in human history. We are building AI Factories , not just data centers.\n \n Silicon-Up Innovation: Work directly with the latest NVIDIA architectures.\n Impact: You will be the architect of the environment that enables the next new discovery.\n \n Velocity: We move at the speed of the researchers we support, bypassing legacy cloud bottlenecks to deliver raw power.\n The base salary range for this role is $233,000 to $341,000. The starting salary will be determined based on job-related knowledge, skills, experience, and market location. We strive for both market alignment and internal equity when determining compensation. In addition to base salary, our total rewards package includes a discretionary bonus, equity awards, and a comprehensive benefits program (all based on eligibility).  \n What We Offer \n The range we’ve posted represents the typical compensation range for this role. To determine actual compensation, we review the market rate for each candidate which can include a variety of factors. These include qualifications, experience, interview performance, and location.\n In addition to a competitive salary, we offer a variety of benefits to support your needs, including:\n \n Medical, dental, and vision insurance - 100% paid for by CoreWeave\n Company-paid Life Insurance \n Voluntary supplemental life insurance \n Short and long-term disability insurance \n Flexible Spending Account\n Health Savings Account\n Tuition Reimbursement \n Ability to Participate in Employee Stock Purchase Program (ESPP)\n Mental Wellness Benefits through Spring Health \n Family-Forming support provided by Carrot\n Paid Parental Leave \n Flexible, full-service childcare support with Kinside\n 401(k) with a generous employer match\n Flexible PTO\n Catered lunch each day in our office and data center locations\n A casual work environment\n A work culture focused on innovative disruption\n \n Our W","salary_min":233000,"salary_max":341000,"location":"Sunnyvale, CA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","gpu","distributed-systems","pre-training","generative-ai","research","infrastructure"],"apply_url":"https://coreweave.com/careers/job?4665964006\u0026board=coreweave\u0026gh_jid=4665964006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-23T19:49:22Z","expires_at":"2026-06-29T14:04:53.609838Z","created_at":"2026-04-13T09:40:47.925505Z","updated_at":"2026-05-30T14:04:53.722794Z","company_name":"CoreWeave","company_slug":"coreweave","company_logo_url":"https://www.google.com/s2/favicons?domain=coreweave.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4967c4a2-2640-422f-bb55-49803f326195"},{"id":"ef675042-a6f1-455f-987f-80af37d364d2","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Machine Learning Engineer, Geometry Team","slug":"machine-learning-engineer-geometry-team-38491221","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the autonomously driving vehicle (ADV), i.e., the system that “perceives” the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware.\n The Geometry team’s mission is to provide an early layer of perception for background subtraction, ground segmentation, and obstacle generation. Projects on our team generally require a diverse skill set of ML and geometric algorithms, onboard sensing and offboard mapping, and the ability to write and maintain efficient robust code. For this role, we are looking for a strong Software Engineer with robotics and machine learning experience, who will help us to accelerate the transition of low-level Perception tasks from algorithms to machine learning. \n You will:\n \n Combine ML and geometric algorithms to solve 3D spatial reasoning problems.\n Apply machine learning techniques to build multi-modal sensor fusion architectures and spatial-temporal representation learners for object detection and tracking, occupancy and semantic segmentation, road understanding, etc.\n Develop scalable recipes for large data, large model training running on Alphabet’s compute infrastructure, create methods and recipes for pre-training and post-training.\n Develop methods and recipes for distributed fine-tuning enabling multiple developers to simultaneously improve the model, develop methods and recipes to avoid regression against a production system.\n Develop and maintain model evaluation recipes and metrics for measuring and improving performance of pre-trained and fine-tuned models\n \n You have:\n \n Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience\n 3+ years experience in Machine Learning and/or Computer Vision\n Experience with Python and familiarity with C++\n Experience with ML frameworks like PyTorch, JAX, or Tensorflow.\n \n We prefer:\n \n MS or PhD Degree in Machine Learning, Robotics, Computer Science or a similar discipline\n \n In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:\n \n Health, dental, vision, life, disability insurance\n Retirement Benefits: 401(k) with company match\n Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment\n Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary)\n Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks\n Baby Bonding Leave: 18 weeks\n Holidays: 13 paid days per year\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":"Kirkland, WA","workplace":"onsite","job_type":"full-time","experience_level":"mid","tags":["fine-tuning","deep-learning","tensorflow","computer-vision","autonomous-vehicles","pre-training","pytorch","robotics"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7733702","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-19T00:20:05Z","expires_at":"2026-06-29T14:04:24.76605Z","created_at":"2026-05-29T14:12:17.116418Z","updated_at":"2026-05-30T14:04:24.877412Z","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/ef675042-a6f1-455f-987f-80af37d364d2"},{"id":"32b02265-9976-4081-aa72-9be045b8ef05","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Imagine Model","slug":"member-of-technical-staff-imagine-model-1dbfa2c4","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 As a multimodal engineer on the Imagine Model Team, you will develop cutting-edge AI experiences beyond text, with a strong focus on enabling high-fidelity understanding and generation across image and video modalities, while also incorporating audio where it enhances visual content (e.g., synchronized audio for video). Responsibilities span data curation, modeling, training, inference serving, and product integration, covering both pretraining and post-training phases. You will collaborate closely with product teams to push model frontiers and deliver exceptional end-to-end user experiences.\n RESPONSIBILITIES:\n \n Create and drive engineering agendas to advance multimodal capabilities, with emphasis on image and video generation, editing, understanding, controllable/long-horizon synthesis, agentic planning, RL training, and world simulation (including audio integration for richer video experiences).\n Improve data quality through annotation, filtering, augmentation, synthetic generation, captioning, and in-depth data studies, particularly for visual and audio data. \n Design evaluation frameworks, metrics, benchmarks, evals, and reward models tailored to image/video/audio quality and coherence.\n Implement efficient algorithms for state-of-the-art model performance, including real-time inference, distillation, and scalable serving for visual content.\n Develop scalable data collection and processing pipelines for multimodal (primarily image/video-focused) datasets.\n Collaborate cross-functionally to integrate AI solutions into production and rapidly iterate based on user feedback.\n \n BASIC QUALIFICATIONS:\n \n Track record in leading studies that significantly improve neural network capabilities and performance through better data or modeling.\n Experience in data-driven experiment designs, systematic analysis, and iterative model debugging.\n Experience developing or working with large-scale distributed machine learning systems.\n Ability to deliver optimal end-to-end user experiences.\n Hands-on contributor with initiative, excellence, strong work ethic, prioritization skills, and excellent communication.\n \n PREFERRED SKILLS AND EXPERIENCE:\n \n Experience in SFT, RL, evals, human/synthetic data collection, or agentic systems.\n Proficiency in Python, JAX/XLA, PyTorch, Rust/C++, Spark, Ray, and related large-scale frameworks.\n Domain expertise in multimodal applications such as graphics engines, rendering techniques, image/video understanding and generation, world models, real-time simulation, or controllable/long-horizon visual content creation (audio/speech processing or music/audio generation experience is a plus where it supports video).\n Experience with agentic RL training, controllable/long-horizon generation, or multimodal agents that reason and act across modalities (especially in visual domains).\n \n COMPENSATION AND BENEFITS:\n $180,000 - $440,000 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":180000,"salary_max":440000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["pytorch","pre-training","computer-graphics","deep-learning","agents","reinforcement-learning"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5051985007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-06T00:44:12Z","expires_at":"2026-06-29T14:02:57.701357Z","created_at":"2026-04-13T09:38:41.622384Z","updated_at":"2026-05-30T14:02:57.810404Z","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/32b02265-9976-4081-aa72-9be045b8ef05"},{"id":"a785716f-4e70-48c9-a346-d1e8b86850cc","company_id":"75dcf7c0-5121-45f1-8d1b-6bfbfe15072f","title":"Helix AI Engineer, Agentic Systems","slug":"helix-ai-engineer-agentic-systems-0d8f96b3","description":"Figure AI is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human-level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA.\n Our goal is to create embodied AI systems that can perceive the world through pixels, reason over memory, and reliably execute complex tasks over minutes to hours in real environments. We are looking for a Helix AI Engineer, Agentic Systems experienced in building multimodal reasoning systems—agents that operate autonomously from raw sensory input, maintain episodic memory, plan over long horizons, and execute reliably within structured evaluation harnesses, e.g. pixels-to-actions computer use agents. This role focuses on developing the agent architectures and infrastructure that enable robots to function as persistent, reliable embodied agents in the real world.\n Responsibilities \n \n Design, train, and deploy multimodal agents that operate autonomously for hours to days\n Build agents that reason from raw sensory inputs (pixels, environment state, proprioception) to structured actions\n Implement episodic memory systems for persistent state, retrieval, and long-horizon reasoning\n Develop planning, reasoning, and tool-use mechanisms for multi-step task execution\n Build reliable perception → reasoning → action loops with strong stability and failure recovery\n Design evaluation harnesses, benchmarks, and metrics to measure agent reasoning, planning, and reliability\n Design and run data studies across the training lifecycle , including pretraining, mid-training, and post-training\n Apply reinforcement learning, reward modeling, and post-training techniques to improve agent reasoning and reliability in real-world environments\n Develop evaluation frameworks and benchmarks to measure robot reasoning, planning, and task success across diverse scenarios \n Build infrastructure for scalable model training, distributed experimentation, and agent evaluation \n Work closely with other teams to integrate agent models into the full humanoid autonomy stack\n \n Requirements \n \n Experience building autonomous agents that run continuously and complete multi-step tasks\n Experience developing agents that reason from pixel inputs or raw environment observations\n Experience implementing agent memory, planning, reasoning, or tool-use systems\n Experience training or fine-tuning multimodal or foundation models\n Strong proficiency in Python and modern deep learning frameworks (e.g., PyTorch)\n Strong experimental rigor and ability to design, analyze, and iterate on ML systems\n Strong software engineering skills and ability to build reliable, maintainable systems\n Ability to work independently and own complex technical problems end-to-end\n \n Bonus Qualifications \n \n Experience with embodied AI, robotics learning, or robot policy training \n Experience building multimodal foundation models (vision-language or vision-language-action) \n Background in agentic AI systems or long-horizon planning architectures \n Experience working with large-scale distributed training systems \n Publication record in machine learning, robotics, or embodied AI\n Passion for building autonomous humanoid robots that operate in the real world\n \n  \n The US base salary range for this full-time position is between $150,000 - $350,000 annually.\n The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.","salary_min":150000,"salary_max":350000,"location":"San Jose, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["agents","distributed-systems","deep-learning","fine-tuning","generative-ai","pytorch","robotics","pre-training"],"apply_url":"https://job-boards.greenhouse.io/figureai/jobs/4659175006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-03-02T20:28:07Z","expires_at":"2026-06-29T14:05:53.355764Z","created_at":"2026-04-13T09:42:02.982187Z","updated_at":"2026-05-30T14:05:53.465912Z","company_name":"Figure AI","company_slug":"figure-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=figure.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a785716f-4e70-48c9-a346-d1e8b86850cc"},{"id":"48ab96d5-7535-4f33-be4e-3a609c828657","company_id":"d49c7f16-1314-459a-acab-7b3d38ee01a9","title":"Member of Technical Staff, Pre-training Systems","slug":"member-of-technical-staff-pre-training-systems-72fbf71a","description":"Magic’s mission is to build safe AGI that accelerates humanity’s progress on the world’s most important problems. We believe the most promising path to safe AGI lies in automating research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach combines frontier-scale pre-training, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.\n\n\n\n\nABOUT THE ROLE\n\nAs a Software Engineer on the Pre-training Systems team, you will design and operate the distributed infrastructure that trains Magic’s long-context models at scale.\n\nThis role focuses on large-scale model training across massive GPU clusters. You will work at the boundary between deep learning and distributed systems, ensuring that training runs are performant, reliable, and reproducible under extreme scale.\n\nMagic’s long-context models create non-trivial systems challenges: sustained memory pressure, communication overhead across thousands of devices, long-running jobs that must survive failures, and efficient sequence packing under hardware constraints. You will own the systems that make large-scale pre-training stable and fast.\n\n\n\n\nWHAT YOU’LL WORK ON\n\n - Scale distributed training across large GPU clusters (data, tensor, pipeline parallelism)\n\n - Optimize communication patterns and gradient synchronization\n\n - Improve checkpointing, fault tolerance, and job recovery systems\n\n - Profile and eliminate performance bottlenecks across compute, networking, and storage\n\n - Improve experiment reproducibility and orchestration workflows\n\n - Increase hardware utilization and training throughput\n\n - Collaborate with Kernels and Research to align model architecture with systems realities\n   \n   \n\n\nWHAT WE’RE LOOKING FOR\n\n - Strong software engineering and distributed systems fundamentals\n\n - Experience training large models in multi-node GPU environments\n\n - Deep understanding of parallelism strategies and performance trade-offs\n\n - Experience debugging cross-layer issues in production ML systems\n\n - Strong ownership mindset and ability to operate critical infrastructure\n\n - Track record of improving performance or reliability of large-scale systems\n   \n   \n\n\nCOMPENSATION, BENEFITS, AND PERKS (US):\n\n - Annual salary range: $225K - $550K\n\n - Equity is a significant part of total compensation, in addition to salary\n\n - 401(k) plan with 6% salary matching\n\n - Generous health, dental and vision insurance for you and your dependents\n\n - Unlimited paid time off\n\n - Visa sponsorship and relocation stipend to bring you to SF, if possible\n\n - A small, fast-paced, highly focused team\n\nMagic strives to be the place where high-potential individuals can do their best work. We value quick learning and grit just as much as skill and experience.\n\n\n\n\nOUR CULTURE\n\n - Integrity. Words and actions should be aligned\n\n - Hands-on. At Magic, everyone is building\n\n - Teamwork. We move as one team, not N individuals\n\n - Focus. Safely deploy AGI. Everything else is noise\n\n - Quality. Magic should feel like magic","salary_min":225000,"salary_max":550000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["pre-training","deep-learning","code-generation","distributed-systems","gpu"],"apply_url":"https://jobs.ashbyhq.com/magic.dev/f1d3988f-f93c-42b7-ad1a-f9fb3d07ff26/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-02-28T00:36:33.221Z","expires_at":"2026-06-29T14:05:05.63634Z","created_at":"2026-04-13T09:41:02.485637Z","updated_at":"2026-05-30T14:05:05.755353Z","company_name":"Magic","company_slug":"magic","company_logo_url":"https://www.google.com/s2/favicons?domain=magic.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/48ab96d5-7535-4f33-be4e-3a609c828657"},{"id":"456c9786-1c96-4d96-b7f9-155b3e94cc1d","company_id":"d49c7f16-1314-459a-acab-7b3d38ee01a9","title":"Member of Technical Staff, Inference \u0026 RL Systems","slug":"member-of-technical-staff-inference-rl-systems-f9ee45d5","description":"Magic’s mission is to build safe AGI that accelerates humanity’s progress on the world’s most important problems. We believe the most promising path to safe AGI lies in automating research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach combines frontier-scale pre-training, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.\n\n\n\n\nABOUT THE ROLE\n\nAs a Software Engineer on the Inference \u0026 RL Systems team, you will design and operate the distributed systems that serve our models in production and power large-scale post-training workflows.\n\nThis role sits at the boundary between model execution and distributed infrastructure. You will work on systems that determine inference latency, throughput, stability, and the reliability of RL and post-training training loops.\n\nMagic’s long-context models introduce demanding execution constraints: KV-cache scaling, memory pressure under long sequences, batching trade-offs, long-horizon trajectory rollouts, and sustained throughput under real-world workloads. You will own the infrastructure that makes both production inference and large-scale RL iteration fast and reliable.\n\n\n\n\nWHAT YOU’LL WORK ON\n\n - Design and scale high-performance inference serving systems\n\n - Optimize KV-cache management, batching strategies, and scheduling\n\n - Improve throughput and latency for long-context workloads\n\n - Build and maintain distributed RL and post-training infrastructure\n\n - Improve reliability of rollout, evaluation, and reward pipelines\n\n - Automate fault detection and recovery for serving and RL systems\n\n - Profile and eliminate performance bottlenecks across GPU, networking, and storage layers\n\n - Collaborate with Kernels and Research to align execution systems with model architecture\n   \n   \n\n\nWHAT WE’RE LOOKING FOR\n\n - Strong software engineering and distributed systems fundamentals\n\n - Experience building or operating large-scale inference or training systems\n\n - Deep understanding of GPU execution constraints and memory trade-offs\n\n - Experience debugging performance issues in production ML systems\n\n - Ability to reason about system-level trade-offs between latency, throughput, and cost\n\n - Track record of owning critical production infrastructure\n\n\n\n\nCOMPENSATION, BENEFITS, AND PERKS (US)\n\n - Annual salary range: $225K - $550K\n\n - Equity is a significant part of total compensation, in addition to salary\n\n - 401(k) plan with 6% salary matching\n\n - Generous health, dental and vision insurance for you and your dependents\n\n - Unlimited paid time off\n\n - Visa sponsorship and relocation stipend to bring you to SF, if possible\n\n - A small, fast-paced, highly focused team\n\nMagic strives to be the place where high-potential individuals can do their best work. We value quick learning and grit just as much as skill and experience.\n\n\n\n\nOUR CULTURE\n\n - Integrity. Words and actions should be aligned\n\n - Hands-on. At Magic, everyone is building\n\n - Teamwork. We move as one team, not N individuals\n\n - Focus. Safely deploy AGI. Everything else is noise\n\n - Quality. Magic should feel like magic","salary_min":225000,"salary_max":550000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","pre-training","code-generation","inference"],"apply_url":"https://jobs.ashbyhq.com/magic.dev/427ffdee-d4d1-4a39-a730-4a96435daa67/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-02-28T00:34:41.815Z","expires_at":"2026-06-29T14:05:05.551796Z","created_at":"2026-04-13T09:41:02.40373Z","updated_at":"2026-05-30T14:05:05.666034Z","company_name":"Magic","company_slug":"magic","company_logo_url":"https://www.google.com/s2/favicons?domain=magic.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/456c9786-1c96-4d96-b7f9-155b3e94cc1d"},{"id":"b7fdeac3-727e-40cd-bee8-3d8746a1e41b","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Senior Machine Learning Engineer – VLM/LLM Evaluation","slug":"senior-machine-learning-engineer-vlmllm-evaluation-4134a964","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.\n This role follows a hybrid work schedule and you will report to a Senior Staff Software Engineer.\n You will: \n \n Work with a creative team of people who help to build the state-of-the-art Foundation Models that are used throughout Waymo’s systems, both onboard autonomous vehicles and offboard in simulation\n Drive the development or significantly contribute to end-to-end evaluation systems and benchmarks for Waymo Foundation models, encompassing the entire life-cycle from pre-training and supervised fine-tuning (SFT) to reinforcement learning (RL), for evaluating the quality, safety, and realism of embodied AI agents\n Partner with cross-functional teams within the organization to land innovative tech in production\n Implement and extend large large scale data and evaluation pipelines.\n \n You have: \n \n Bachelor or Master’s degree in Computer Science, similar technical field of study, or equivalent practical experience\n Experience in ML engineering and applied Deep Learning\n Experience with large scale distributed system\n Proficient programming skills (eg: Python, C/C++)\n \n We prefer: \n \n ML infra experience: training, evaluating and deploying ML models at scale\n Deep learning experience, especially with generative models, e.g., LLMs/VLMs, and/or reinforcement learning\n Proficiency and in-depth knowledge of the inner workings of an ML framework (e.g. Pytorch, JAX, Tensorflow) \n \n In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:\n \n Health, dental, vision, life, disability insurance\n Retirement Benefits: 401(k) with company match\n Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment\n Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary)\n Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks\n Baby Bonding Leave: 18 weeks\n Holidays: 13 paid days per year\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 $204,000 — $259,000 USD","salary_min":204000,"salary_max":259000,"location":"Mountain View, CA","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["deep-learning","llm","reinforcement-learning","pre-training","agents","fine-tuning","distributed-systems","generative-ai"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=7644924","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-02-23T19:28:40Z","expires_at":"2026-06-29T14:04:27.207435Z","created_at":"2026-04-13T09:40:17.444855Z","updated_at":"2026-05-30T14:04:27.312964Z","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/b7fdeac3-727e-40cd-bee8-3d8746a1e41b"},{"id":"6c529bac-1e8e-4cb7-84bb-8d00e382effb","company_id":"e597788a-bd36-460e-8d1a-40fdbfbcc5c3","title":"Senior Applied AI Engineer - Multimodal Transformers","slug":"applied-ai-engineer-multimodal-transformers-a753202a","description":"Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi-truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense.\n Kodiak's autonomy stack is built on AI that fuses diverse sensor streams into a unified, actionable understanding of the world. We are developing GigaFusionNet – a large-scale multimodal transformer that learns rich, joint representations across camera, LiDAR, and radar through attention-based fusion. We are looking for engineers to push the boundaries of how transformer architectures combine and reason over heterogeneous sensor data.This role is open to all levels – from those eager to contribute to cutting-edge research to experts driving innovation at scale. In this role, you will: \n \n Design and develop multimodal transformer architectures that fuse camera, LiDAR, and radar into unified representations \n Research and implement cross-modal attention mechanisms, token fusion strategies, and efficient multi-stream tokenization \n Build scalable training pipelines for large-scale multimodal transformers across massive real-world datasets \n Explore self-supervised and contrastive pretraining objectives that learn transferable multimodal representations \n Optimize transformer models for real-time inference under latency and compute constraints \n \n What you’ll bring: \n \n BS, MS, or PhD in AI, Computer Science, or a related field \n 4+ years experience with transformer architectures, particularly in multimodal or multi-stream settings \n Familiarity with cross-attention, token fusion, or modality alignment techniques \n Proficiency in Python and deep learning frameworks like PyTorch or TensorFlow \n Strong understanding of scalable training for large models, including distributed training and mixed-precision optimization \n Passion for building AI that reasons over the full breadth of sensory input to operate safely in the real world \n \n What we offer: \n \n Competitive compensation package including equity and annual bonuses \n Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and  MetLife (including a medical plan with infertility benefits) \n MetLife Legal Services, Identity \u0026 Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, \u0026 Critical Illness Insurance \n Flexible PTO, 10 paid holidays, and generous parental leave policies \n Our office is centrally located in Mountain View, CA \n Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging \n Long Term Disability, Short Term Disability, Life Insurance \n Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation)  \n Fidelity 401(k) \n Commuter, FSA, Dependent Care FSA, HSA \n Various incentive programs (referral bonuses, patent bonuses, etc.) \n The pay range listed below reflects the base salary  in our SF/Silicon Valley location,  across several internal levels. Actual starting pay will be based on job-related factors including: work location, experience, relevant training, education, skill level and performance during interview. Total compensation at Kodiak includes base pay, equity, bonus and a competitive benefits package\n California Pay Range\n $200,000 — $260,000 USD \n  \n At Kodiak, we strive to build a diverse community working towards our common company goals in a safe and collaborative environment where harassment of any kind is strictly prohibited. Kodiak is committed to equal opportunity employment regardless of race, ethnicity, religion, gender identity, sexual orientation, age, disability, or veteran status, or any other basis protected by applicable law.\n  \n In alignment with its business operations, Kodiak adheres to all relevant statutes, regulations, and administrative prerequisites. Accordingly, roles that carry more sensitive requirements may be limited to candidates that can satisfy additional scrutiny and eligibility for such positions may hinge on verification of a candidate’s residence, U.S. person status, and/or citizenship status. Should the position require, and Kodiak determines that a candidate’s residence, U.S. person status, and/or citizenship status necessitate an export license, bar the candidate from the position, or otherwise fall under national security-related restrictions, Kodiak will consider the candidate for alternative positions unaffected by such restrictions, under ter","salary_min":200000,"salary_max":260000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["deep-learning","robotics","autonomous-vehicles","tensorflow","distributed-systems","pytorch","pre-training","data-pipeline"],"apply_url":"https://job-boards.greenhouse.io/kodiak/jobs/4139994009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-02-20T02:39:09Z","expires_at":"2026-06-29T14:08:18.58799Z","created_at":"2026-04-13T09:41:39.918453Z","updated_at":"2026-05-30T14:08:18.699683Z","company_name":"Kodiak","company_slug":"kodiak","company_logo_url":"https://www.google.com/s2/favicons?domain=kodiak.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6c529bac-1e8e-4cb7-84bb-8d00e382effb"},{"id":"75c77312-2dc1-4725-9530-219afa3ad238","company_id":"ff51c80a-dce9-4cb4-b2e6-9c060d25ef55","title":"Research Scientist - 3D Vision and Generation, Self-Driving","slug":"research-scientist-3d-vision-and-generation-self-driving-df2ed064","description":"About Applied Intuition\n Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co .\n We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. \n About the role and team \n We are looking for multiple passionate Research Scientists to join the Research Group at Applied Intuition. The mission of the group is to create cutting-edge technology enabling next-generation physical AI, with emphasis on the two most challenging applications reshaping our everyday life: end-to-end autonomous driving and robotic generalist. We have a group composed of leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions—including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA. Learn more at appliedintuition.com/research .\n Supported by industry-leading tools and infra, researchers can access millions of miles of data from large fleets, and deploy methods they develop into various autonomous and robotic systems including self-driving cars/trucks, autonomous mining/construction machines, humanoid robots and dexterous hands. In addition to your research contributions, you will contribute to and learn from best practices in the autonomy and robotics industries within our fast-paced and customer-focused culture. Improvements deployed to our system immediately help our customers with their programs and deliver value to our business.\n We are open to all years of experience as long as the necessary requirements are met, including those with potential Tech Lead and Manager capacity. \n At Applied Intuition, you will: \n \n Conduct research on 3D vision related topics including 3D/world-action foundation model, multi-modal pretraining, feed-forward Gaussian splatting, world foundation model with applications to autonomous driving, and fundamental topics on 3D vision and generation with broader applications\n Work closely with other Research Scientists and interns on research publications for submission to top-tier conferences\n Collaborate with Research Engineers and engineering teams to test and deploy algorithms to our autonomy and simulation products\n \n We’re looking for someone who has: \n \n Strong research record in the fields of 3D vision, reconstruction and generation for autonomous systems and robotics, with publications in top-tier conferences or journals in the fields of computer vision, machine learning, and robotics\n MSc or PhD in machine learning and computer vision with autonomy and robotics applications or closely-related fields\n Passion for next-generation, scalable autonomy and robotics for real-world systems\n Strong research skills and the ability to work both independently and collaboratively on projects\n Technical experience in: Python, Pytorch, computer vision, robotics systems, and distributed machine learning model training\n \n Nice to have: \n Hands-on experience in at least one of the following fields:\n \n 3D foundation model and pretraining\n Multi-modal foundation model\n Feed-forward Gaussian splatting and reconstruction\n World foundation model and generation\n 3D/multi-view end-to-end models for autonomous driving or robotics\n \n Compensation at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of options and/or restricted stock units, comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off. Note that benefits are subject to change and may vary based on jurisdiction of employment.\n Applied Intuition pay ranges reflect the minimum and maximum intended target base salary for new hire salaries for the position. The actual base sa","salary_min":126000,"salary_max":423000,"location":"Sunnyvale, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["robotics","pytorch","autonomous-vehicles","search","generative-ai","cloud","computer-vision","pre-training"],"apply_url":"https://boards.greenhouse.io/appliedintuition/jobs/4662452005?gh_jid=4662452005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-02-13T21:27:22Z","expires_at":"2026-06-29T14:03:36.020651Z","created_at":"2026-04-13T09:39:20.088492Z","updated_at":"2026-05-30T14:03:36.135946Z","company_name":"Applied Intuition","company_slug":"applied-intuition","company_logo_url":"https://www.google.com/s2/favicons?domain=appliedintuition.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/75c77312-2dc1-4725-9530-219afa3ad238"}],"page":1,"per_page":20,"total":155,"total_pages":8}