{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL (Reinforcement Learning) ","slug":"research-engineer-performance-rl-2f0da25a","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL Teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:\n \n \n Developing systems that enable models to use computers effectively\n \n Advancing code generation through reinforcement learning\n \n Pioneering fundamental RL research for large language models\n \n Building scalable RL infrastructure and training methodologies\n \n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the Role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.\n You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:\n \n \n Invent, design and implement RL environments and evaluations.\n \n Conduct experiments and shape our research roadmap.\n \n Deliver your work into training runs.\n \n Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.\n \n You may be a good fit if you:\n \n \n Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).\n \n Have worked across the stack – kernels, model code, distributed systems.\n \n Know how to balance research exploration with engineering implementation.\n \n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have:\n \n \n Experience with reinforcement learning.\n \n Experience porting ML workloads between different types of accelerators.\n \n Familiarity with LLM training methodologies.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $350,000 — $850,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","code-generation","search","pytorch","llm","jax","fine-tuning","gpu"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","is_featured":true,"is_sticky":true,"status":"active","published_at":"2026-03-23T16:27:59Z","expires_at":"2026-08-15T14:00:29.666185Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-07-16T14:00:29.796553Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/48720738-0f4b-483d-9739-14039ae457d0"},{"id":"f47b2b52-9138-4056-a197-783873a96c39","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Voice Model","slug":"member-of-technical-staff-voice-model-5b5f6cb9","description":"SpaceXAI’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 SpaceXAI, 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 SpaceXAI 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","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","pre-training","pytorch","speech","reinforcement-learning","fine-tuning"],"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":"2026-08-15T14:03:44.876863Z","created_at":"2026-04-13T09:38:43.3144Z","updated_at":"2026-07-16T14:03:45.007782Z","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","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["alignment","llm","pre-training","search","agents","reinforcement-learning","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":"2026-08-15T14:00:31.404308Z","created_at":"2026-04-13T09:36:01.625992Z","updated_at":"2026-07-16T14:00:31.517772Z","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":"66be6f1d-738c-4b9b-b07d-4cae69e7b29d","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Machine Learning Engineer, Agent Oversight","slug":"senior-machine-learning-engineer-agent-oversight-774633fc","description":"About Scale\n Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.\n About the Team\n Applied Intelligence Systems team is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like.\n About the Role\n As a Machine Learning Engineer on Agent Oversight, you will drive the end-to-end lifecycle that ensures our production agents perform reliably and improve over time. This includes building observability tools, designing robust evaluation frameworks, and developing improvement loops. Whether scaling infrastructure or researching new improvement methods, you will navigate the entire ML loop while maintaining rigorous technical standards.\n You will:\n \n Build or contribute to observability into agent behavior in production — the signals and instrumentation needed to actually see what an agent is doing, not just whether it succeeded or failed\n Design evaluation methodologies and metrics for agentic applications, and work with the platform to make them run automatically, at scale, across different customer use cases, not just as one-off analyses\n Build, ship, and own ML systems that detect drift, anomalies, or misalignment in production agent behavior — from first prototype through running reliably at scale\n Design and run rigorous experiments to validate model and agent performance improvements before they ship\n Work alongside software engineers on the platform where your work intersects with broader infrastructure — but you’re expected to take your own work from idea to production, not hand it off\n Collaborate closely with product managers, customers, data annotators, Forward Deployed Engineers, and other engineering teams to translate enterprise and government requirements into robust platform capabilities\n Depending on focus, contribute to novel methods and approaches that push the state of the art for agent evaluation and improvement, or focus on building ML systems that hold up reliably at scale in production\n \n Requirements:\n \n 5+ years of experience as an ML engineer or applied scientist, ideally on a production ML or LLM-powered system — not just consuming a third-party ML API within a feature\n Strong grounding in  at least two  of the following:\n \n Building or scaling evaluation, monitoring, or continuous-learning infrastructure for ML/agentic systems\n Design experience for agent systems (architecture, orchestration, tool use)\n Developing new methods, reward models, or model training/fine-tuning approaches\n \n Hands-on experience with LLMs and agent architectures — tool use, planning, multi-agent orchestration\n Comfortable partnering with software engineers to productionize research and experimental work, not just deliver a one-off analysis\n Rigorous approach to experimentation: clear hypotheses, real statistical grounding, and results that hold up under scrutiny\n Track record of collaborating across functions (Product, Forward Deployed Engineering, etc.) to navigate ambiguous requirements and bring them to production\n Gives direct, substantive feedback on designs and code, and takes it the same way — and mentors others as they grow\n \n Nice to have:\n \n Experience building or contributing to RLHF, SFT, or other fine-tuning/RL workflows, reward modeling, or verifiable-reward systems\n Experience with model or systems optimization (e.g., latency, cost, or inference efficiency)\n Published research, open-source contributions, or patents in agentic systems, LLMs, or applied ML\n Experience working in regulated or enterprise contexts\n Track record of taking a novel method from prototype to something running reliably in production, navigating ambiguity along the way\n Experience reviewing others’ technical designs or mentoring engineers at a senior/staff level\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 level","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","reinforcement-learning","agents","generative-ai","autonomous-vehicles","fine-tuning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4714527005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:14:32Z","expires_at":"2026-08-15T14:01:43.524986Z","created_at":"2026-07-15T14:01:47.280877Z","updated_at":"2026-07-16T14:01:43.716585Z","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/66be6f1d-738c-4b9b-b07d-4cae69e7b29d"},{"id":"724ab66f-d426-4c39-b31e-1b432c22d253","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Senior Simulation Vehicle Modeling Engineer","slug":"senior-simulation-vehicle-modeling-engineer-9867b4f8","description":"About the Company  \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight. Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer. \n Meet The Team:   \n At Torc, our simulation teams are building the foundation for AV 3.0 driver-out development and certification. This team serves as the critical technical interface between simulation/modeling and autonomy consumers. By ensuring model fidelity, coverage, and usability, the team plays a vital role in evaluating and advancing autonomous driving software. We support massive-scale, parallel execution for scaled verification and validation (V\u0026V) and high-throughput reinforcement learning (RL) training. Ultimately, we provide the framework and tooling that serves Autonomy, Safety, and Release with shared artifacts, common metrics, and deterministic reproducibility.    \n What You'll Do:   \n \n Develop and own vehicle dynamics models, including high-fidelity truck models that accurately represent longitudinal, lateral, and transient behavior for use in simulation and autonomy validation.  \n Design, develop, and maintain high-fidelity simulation platforms used to validate autonomous driving software in closed-loop evaluation pipelines at scale.   \n Partner with autonomy engineering teams to capture and implement vehicle model requirements that support planning, controls, and system-level V\u0026V activities.   \n Own model capability communication, known limitations, and release notes to enable effective autonomy validation.   \n Ensure vehicle model updates do not regress autonomy V\u0026V system performance by using automated regression frameworks.   \n Execute full software development lifecycle activities primarily in C++ within a Linux development environment and ROS/ROS2 tooling, applying Lean-Agile methodologies.   \n Perform root cause analysis on issues identified during simulation runs and hardware-in-the-loop testing.   \n Participate in designing test plans for data acquisition and telemetry that support field data collection for vehicle model refinement.   \n Support system-level test plans and verification strategies.   \n Communicate progress, design decisions, and blockers clearly at daily stand-ups and design reviews.   \n Build and maintain collaborative relationships with OEM partners and simulation tool vendors to evaluate, integrate, and co-develop simulation capabilities.   \n \n What You'll Need to Succeed:   \n \n Bachelor's Degree in Computer Science, Robotics, Mechanical Engineering, Electrical Engineering, or a related technical field plus 6+ years of relevant experience; or Master's Degree in the above fields plus 3+ years of relevant experience; or  PhD in the above fields plus 1+ years of relevant experience.   \n Proficiency in C++ (primary), Python for tooling and ML integrations, ROS/ROS2, CMake, and Linux.   \n Physics-based modeling of ground vehicles, including longitudinal/lateral dynamics, tire models, and powertrain.   \n Working knowledge of AV autonomy stack architecture—planning, controls, and system integration—to collaborate effectively with autonomy engineering teams and ensure vehicle models meet V\u0026V requirements.   \n Ability to translate vehicle model capabilities and limitations to autonomy engineering teams, and to capture their requirements to inform model fidelity improvement initiatives.   \n Experience with unit, integration, and regression testing, as well as automated validation pipelines and simulation-based performance benchmarking.   \n Expected to drive consensus across simulation, autonomy, controls, and product engineering \u0026 safety teams.   \n Ability to own and maintain key technical systems across multiple repositories and contribute to cross-org architectural decisions.   \n Must operate as an advanced-level professional with wide latitude for independent judgment and minimal supervision.   \n Willingness to mentor and guide engineers within the group and contribute to technical direction within the simulation and autonomy domains.   \n \n Bonus Points!   \n \n Experience with high-fidelity truck \u0026 trailer or heavy-vehicle models.  \n Experience building or extending closed-loop autonomous vehicle simulation environments.   \n Familiarity with scenario-based validation and sim-to-real correlation activities.   \n Familiarity with how learned models (neural networks) consume simulation and vehicle model outputs in autonomy V\u0026V workflows.   \n \n Perks of Being a Full-time Torc’r   \n Torc cares about our team members and we strive","salary_min":160800,"salary_max":193000,"location":"Ann Arbor, MI","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["robotics","deep-learning","payments","reinforcement-learning","autonomous-vehicles"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8624508002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T18:27:34Z","expires_at":"2026-08-15T14:06:21.505391Z","created_at":"2026-07-15T14:07:36.22853Z","updated_at":"2026-07-16T14:06:21.625011Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/724ab66f-d426-4c39-b31e-1b432c22d253"},{"id":"7befba03-6985-475e-9441-9bd1ccb173d8","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Chip Design RL (Reinforcement Learning)","slug":"research-engineer-chip-design-rl-reinforcement-learning-39e9d4d0","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the RL teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Fable 5 and Opus 4.8. Our work spans several key areas:\n \n Developing systems that enable models to use computers effectively\n Advancing code generation through reinforcement learning\n Pioneering fundamental RL research for large language models\n Building scalable RL infrastructure and training methodologies\n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to design silicon. Hardware design is difficult and unforgiving – exactly the sort of domain we want Claude to excel at.\n You'll leverage your chip design expertise and turn it into tasks and signals for models to learn from. Specifically, you will: \n \n Invent, design, and implement RL environments and evaluations for agentic RTL generation, design (including formal) verification, physical design optimization.\n Work on cross-cutting RL considerations such as EDA-tool latency optimization and proxy rewards.\n Conduct experiments and shape our roadmap.\n Deliver your work into research and production training runs.\n Collaborate with other researchers and engineers across and outside Anthropic.\n \n You may be a good fit if you: \n \n Have expertise in ASIC or FPGA design: RTL, design verification (UVM, formal methods, coverage-driven), physical design (synthesis, place-and-route, timing closure), PPA optimization, DFT, ECOs.\n Are fluent with industry EDA tools and processes.\n Have taped out chips and have experience going from spec to silicon.\n Know how to balance research exploration with engineering implementation.\n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have: \n \n Experience with reinforcement learning, evaluations or environments.\n Built tooling or automation around chip design flows.\n Worked on ML accelerators or high-performance compute hardware.\n Familiarity with high-level synthesis or architecture simulators.\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","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["fine-tuning","reinforcement-learning","agents","search","alignment","llm","code-generation","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5231612008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T22:19:12Z","expires_at":"2026-08-15T14:00:28.138626Z","created_at":"2026-07-15T14:00:27.407964Z","updated_at":"2026-07-16T14:00:28.258454Z","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/7befba03-6985-475e-9441-9bd1ccb173d8"},{"id":"71ee758f-16cd-4c69-8fc0-3f12619c37ad","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff Machine Learning Engineer, Simulation ","slug":"staff-machine-learning-engineer-simulation-353a3819","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 Driver Understanding and Evaluation team at Waymo develops a rich understanding of Waymo Driver's behavior. With over 1 million driverless miles per week, it is critical that Waymo can understand and assess the behavior of all its vehicles - both in the field and in simulation - with automated algorithms. The learned metrics team is a strategic bet to use machine learning to ensure we can scale to meet Waymo's goals. We collaborate across teams to bring ML to production systems and build what is Waymo's reward function. We build and operate large-scale machine learning and data systems, simulation workflows, and insight tools. We combine expert human judgements and advanced machine learning models to deliver training and evaluation data for the Waymo driver. We are looking for researchers and software engineers who are passionate about developing production grade machine learning systems for our autonomous vehicles and have an incessant drive to improve the performance of our technology stack.\n In this hybrid role, you will report to an Engineering Manager  \n You will: \n \n Report into the TLM for the Learned Metrics Team\n Develop ML models that assess our autonomous vehicle's behavior.\n Develop ML infrastructure to support performant models.\n Collaborate across teams to bring state-of-the-art to production.\n \n You have: \n \n BS in Computer Science, Robotics, Statistics, Physics, Math or another quantitative area, or equivalent work experience\n 5+ years of experience building productionized ML models\n Code and design skills: comfort building production systems (Python / C++)\n Background in applied Deep Learning\n A track record in improving model quality\n \n We prefer: \n \n 8+ years of experience building productionized ML models\n Experience in reinforcement learning, transfer learning, or learning.\n Experience with large scale data and models\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","deep-learning","autonomous-vehicles","robotics","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8056720","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T22:11:43Z","expires_at":"2026-08-15T14:05:13.85177Z","created_at":"2026-07-15T14:06:31.242063Z","updated_at":"2026-07-16T14:05:13.968294Z","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/71ee758f-16cd-4c69-8fc0-3f12619c37ad"},{"id":"cb44c455-97e8-4e00-ab4f-3fab00fa325f","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Staff Machine Learning Engineer","slug":"staff-machine-learning-engineer-586d7131","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Job Duties: Design, develop, and train advanced machine learning models, including deep neural networks, transformer-based architectures, and reinforcement learning systems, to power large-scale online advertising ranking and optimization platforms. Lead the development and optimization of complex feature representations, including high-dimensional embeddings, contextual and temporal signals, and cross-session user behavior modeling. Drive end-to-end model lifecycle execution, including system architecture design, large-scale experimentation, model deployment, performance monitoring, and iterative infrastructure improvements in production environments. Collaborate closely with product, data, and infrastructure engineering teams to translate business objectives into scalable, statistically rigorous modeling solutions. Conduct advanced experiment design and causal analysis to evaluate model impact and inform strategic decisions. Provide technical leadership and mentorship to machine learning engineers and contribute to organization-wide modeling standards, best practices, and long-term technical strategy. Shape the long-term modeling vision across multiple advertising domains, including conversion optimization, application advertising, shopping, and brand advertising. Full-time telecommuting is an option. \n Requirements: Master’s degree in Computer Science, Engineering (any field) or related quantitative discipline and (3) three years of experience in the job offered or related occupation. \n Special Skill Requirements: 1) Python, Java, and Scala; 2) C++, Go, or Rust; 3) major machine learning frameworks and libraries; 4) applied statistics, hypothesis testing and experiment design for online machine learning systems; 5) large-scale data processing and analytics frameworks; 6) deployment and operation of production systems in containerized and distributed environments; 7) Designing and training advanced models, including deep neural networks, transformer-based architectures, and reinforcement learning models; 8) marketplace dynamics, such as real-time bidding (RTB) or pacing control systems; 9) developing and optimizing online advertising systems, including ad ranking, targeting, and market place; 10) providing technical leadership, mentorship, or guidance to other machine learning engineers. Any suitable combination of education, training and/or experience is acceptable. Full-time telecommuting is an option. \n Benefits: \n \n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\n Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support\n Family Planning Support\n Gender-Affirming Care\n Mental Health \u0026 Coaching Benefits\n Flexible Vacation \u0026 Paid Volunteer Time Off\n Generous Paid Parental Leave \n \n Submit a resume with references using the apply button on this posting or by email at:  applicationsreview@reddit.com at Req.# 1016.83.2.\n  \n Pay Transparency: \n This job posting may span more than one career level.\n In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit  https://www.redditinc.com/careers/ .\n To provide greater transparency to candidates, we share base pay ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. \n The base pay range for this position is: $230,000.00 - $322,000.00 USD\n  \n #LI-DNI\n In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.\n During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We ","salary_min":230000,"salary_max":322000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","deep-learning","mlops","reinforcement-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8054426","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T13:51:13Z","expires_at":"2026-08-15T14:09:30.82854Z","created_at":"2026-07-15T14:10:39.537452Z","updated_at":"2026-07-16T14:09:30.952676Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/cb44c455-97e8-4e00-ab4f-3fab00fa325f"},{"id":"cec41a2d-61db-466d-97a8-9cfca9b8f6dd","company_id":"a0000000-0000-0000-0000-000000000003","title":"Staff Software Engineer, Full Stack - Gen AI ","slug":"staff-software-engineer-full-stack-gen-ai-014fb300","description":"Our Generative AI Data Engine powers the world’s most advanced LLMs and generative models through world-class RLHF (Reinforcement Learning with Human Feedback), human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.\n This is a horizontal, high-impact L6 Staff Fullstack Engineer \u0026 Architect position reporting directly to the Director of Contributor Engineering.\n Instead of being tied to a single domain, your scope is spread across all Contributor (CB) teams (including Allocation, Growth,  Trust \u0026 Safety, Pay, and Allocations). Together, these teams power Scale’s AI data operations - from building high-impact datasets that push the boundaries of LLM capabilities, to optimizing contributor onboarding and incentives, to safeguarding data integrity through advanced trust, safety, and security measures. They work at the intersection of ML, operations, and analytics to ensure we deliver the highest-quality data at scale.\n You will act as an organizational architect and tech lead, dynamically embedding yourself into the highest-priority projects across the org to guarantee execution, unblock teams, and successfully ship mission-critical initiatives. Concurrently, you will lead the long-term technical evolution of our stack, transforming the core architecture to ensure it is highly sustainable, scalable, and fundamentally AI-native.\n You will:\n \n Deploy flexibly into critical, fast-moving product initiatives across the CB organization\n Lead the architectural overhaul of our platform infrastructure, making it highly sustainable, robust, and optimized for deep integration with LLMs and foundation models.\n Lead architecture decisions for scalability, reliability, and performance\n Mentor and uplevel engineers across the team\n Partner with product and leadership to shape roadmap and priorities\n Own large, ambiguous problem spaces end-to-end\n Work across backend, frontend, and ML systems\n \n Ideally you'd have: \n \n 7+ years of full-time engineering experience, post-graduation, with a proven track record of operating as a Tech Lead, Architect, or Principal Engineer.\n Track record of shipping high-quality products and features at scale\n Experience tinkering with or productizing LLMs, vector databases, and the other latest AI technologies\n Proficient in Javascript/Typescript, and SQL\n Experience with Kubernetes\n Experience with major cloud providers (AWS, Azure, GCP)\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:\n $252,000 — $315,000 USD \n PLEASE NOTE:  Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. \n About Us: \n At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst \u0026 Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. \n We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.  \n We are committed to wor","salary_min":252000,"salary_max":315000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["embeddings","llm","reinforcement-learning","generative-ai","fullstack"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4713608005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-09T18:54:54Z","expires_at":"2026-08-15T14:01:46.947325Z","created_at":"2026-07-10T14:01:27.296205Z","updated_at":"2026-07-16T14:01:47.089807Z","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/cec41a2d-61db-466d-97a8-9cfca9b8f6dd"},{"id":"8e37b314-f237-44dc-a850-dd58524233c1","company_id":"19a78c6a-11dc-4d21-8273-0d2d2bad39b1","title":"Staff Data Scientist","slug":"staff-data-scientist-9bba8726","description":"Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.\n As a Staff Data Scientist, you’ll lead the design and development of scalable ML systems for use cases such as menu recommendation, demand forecasting, offer targeting, and guest personalization. You will serve as a technical thought partner across teams, set best practices, and influence the roadmap for ML-driven products that support key business outcomes. Your work will directly shape strategic decisions and enhance customer experience at scale.  \n This role is for a current vacancy.\n A day in the life (Responsibilities) \n \n Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives.\n Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights.\n Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy.\n Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization.\n Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise.\n Proactively identify areas where data science can create business value and lead cross-functional efforts to drive those opportunities forward.\n Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team.\n \n  \n What you'll need to thrive (Requirements) \n \n 5+ years of experience in data science with a proven track record of delivering production ML systems that drive measurable impact.\n Deep knowledge of statistical modeling, machine learning (e.g., tree-based models, time series, deep learning), and model evaluation.\n Experience working with real-world product data at scale and translating ambiguous problems into well-scoped ML solutions.\n Experience with distributed data processing and training, real-time inference, and ML Ops frameworks\n Prior experience mentoring other data scientists or acting as a tech lead.\n Experience leading experimentation (e.g., A/B testing), causal inference, and real-time decision systems.\n Proficiency in Python and SQL, and experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).\n Strong grasp of software engineering principles including modular design, version control, testing, and CI/CD.\n Hands-on experience with cloud platforms (preferably AWS), including tools like SageMaker, Athena, Glue, DynamoDB, and Bedrock.\n Excellent communication skills and the ability to influence both technical and non-technical stakeholders.\n Strong business acumen with the ability to align technical solutions with company goals.\n \n Bonus ingredients* : \n \n An advanced degree in Computer Science, Statistics, or a related STEM field is preferred.\n Familiarity with MLOps tooling for monitoring, drift detection, retraining, and explainability.\n Experience fine-tuning LLMs and applying reinforcement learning from human feedback (RLHF) to improve model performance and alignment.\n \n  \n AI at Toast \n At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.\n Our Total Rewards Philosophy  We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at  https://careers.toasttab.com/toast-benefits .\n #LI-Remote\n The base salary range for this role is listed below. The starting salary will be determined based on skills, experience, and geographic location. In addition to base salary, our total rewards components include cash compensation (overtime, bonus/commissions if eligible), equity, and benefits. \n Pay Range \n $127,000 — $203,000 CAD \n How Toast Uses AI in its Hiring Process \n Throughout the hiring process, our goal is to get to know you. We use AI tools to support our recruiters and interviewers with tasks like note-taking, summarization, and documentation of interviews to ensure they can be fully focus","salary_min":127000,"salary_max":203000,"location":"Canada","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["deep-learning","tensorflow","reinforcement-learning","llm","mlops","pytorch","fine-tuning","data-science"],"apply_url":"https://careers.toasttab.com/jobs?gh_jid=8052293","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T20:25:38Z","expires_at":"2026-08-15T14:10:30.495588Z","created_at":"2026-07-09T14:09:45.188959Z","updated_at":"2026-07-16T14:10:30.632055Z","company_name":"Toast","company_slug":"toast","company_logo_url":"https://www.google.com/s2/favicons?domain=pos.toasttab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8e37b314-f237-44dc-a850-dd58524233c1"},{"id":"f04f6e13-ccf2-458b-8576-e7fa94481050","company_id":"19a78c6a-11dc-4d21-8273-0d2d2bad39b1","title":"Staff Data Scientist","slug":"staff-data-scientist-317fda4d","description":"Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.\n As a Staff Data Scientist, you’ll lead the design and development of scalable ML systems for use cases such as menu recommendation, demand forecasting, offer targeting, and guest personalization. You will serve as a technical thought partner across teams, set best practices, and influence the roadmap for ML-driven products that support key business outcomes. Your work will directly shape strategic decisions and enhance customer experience at scale.\n A day in the life (Responsibilities) \n \n Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives.\n Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights.\n Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy.\n Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization.\n Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise.\n Proactively identify areas where data science can create business value and lead cross-functional efforts to drive those opportunities forward.\n Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team.\n \n  \n What you'll need to thrive (Requirements) \n \n 7+ years of experience in data science with a proven track record of delivering production ML systems that drive measurable impact.\n Deep knowledge of statistical modeling, machine learning (e.g., tree-based models, time series, deep learning), and model evaluation.\n Experience working with real-world product data at scale and translating ambiguous problems into well-scoped ML solutions.\n Experience with distributed data processing and training, real-time inference, and ML Ops frameworks\n Prior experience mentoring other data scientists or acting as a tech lead.\n Experience leading experimentation (e.g., A/B testing), causal inference, and real-time decision systems.\n Proficiency in Python and SQL, and experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).\n Strong grasp of software engineering principles including modular design, version control, testing, and CI/CD.\n Hands-on experience with cloud platforms (preferably AWS), including tools like SageMaker, Athena, Glue, DynamoDB, and Bedrock.\n Excellent communication skills and the ability to influence both technical and non-technical stakeholders.\n Strong business acumen with the ability to align technical solutions with company goals.\n Experience building services on top of LLMs in a large scale production environment.\n \n Bonus ingredients* : \n \n An advanced degree in Computer Science, Statistics, or a related STEM field is preferred.\n Familiarity with MLOps tooling for monitoring, drift detection, retraining, and explainability.\n Experience fine-tuning LLMs and applying reinforcement learning from human feedback (RLHF) to improve model performance and alignment.\n \n  \n AI at Toast \n At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.\n Our Total Rewards Philosophy  We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at  https://careers.toasttab.com/toast-benefits .\n #LI-Remote\n The base salary range for this role is listed below. The starting salary will be determined based on skills, experience, and geographic location. In addition to base salary, our total rewards components include cash compensation (overtime, bonus/commissions if eligible), equity, and benefits. You can learn more about how we align pay with local labor markets in our Geographic Pay Zone Philosophy . \n Zone A\n $170,000 — $272,000 USD \n Zone B\n $148,000 — $237,000 USD \n Zone C\n $133,000 — $213,000 USD \n How Toast Uses AI in its Hiring Process \n Throughout ","salary_min":133000,"salary_max":213000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","mlops","pytorch","fine-tuning","reinforcement-learning","tensorflow","deep-learning","data-science"],"apply_url":"https://careers.toasttab.com/jobs?gh_jid=8029049","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T20:23:10Z","expires_at":"2026-08-15T14:10:30.383797Z","created_at":"2026-07-09T14:09:45.268862Z","updated_at":"2026-07-16T14:10:30.539586Z","company_name":"Toast","company_slug":"toast","company_logo_url":"https://www.google.com/s2/favicons?domain=pos.toasttab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f04f6e13-ccf2-458b-8576-e7fa94481050"},{"id":"ffb8f345-cc3f-4a19-b74d-6117413ea12c","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Member of Technical Staff - Training Platform","slug":"member-of-technical-staff-training-platform-bf6e9667","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\n\nROLE IMPACT\n\nYou'll help build our hosted training platform - the product that lets users launch LoRA and full fine-tuning runs on managed GPU clusters with a single API call or a few clicks. The role spans the developer-facing platform and the underlying Kubernetes-based training infrastructure that runs the jobs.\n\n\n\n\nCORE TECHNICAL RESPONSIBILITIES\n\n\n\n\nHOSTED TRAINING INFRASTRUCTURE\n\n - Design and operate Kubernetes-based training and inference orchestration across multi-cluster, multi-cloud GPU fleets\n\n - Build and maintain Helm charts that compose trainers, inference servers, environment servers, and supporting services into reproducible \"Training stacks\"\n\n - Develop the Python control-plane agents that watch pods, report run state to the platform, and keep clusters in sync\n\n - Implement scheduling and autoscaling for heterogeneous hardware (H100/H200/B200) using KEDA, LeaderWorkerSet, taints/tolerations, and gang scheduling\n\n - Run a tight GitOps workflow - every change ships through PRs, Helm values, and CI\n\n - Build node-local model caches, checkpoint pipelines, and shared storage for fast cold starts\n\n - Operate the observability stack (Prometheus, Grafana, Loki, DCGM) and make GPU cluster debugging fast\n\n\nPLATFORM DEVELOPMENT\n\n - Build the developer-facing surfaces for hosted training: job submission, live run monitoring, logs, metrics, model/adapter management, comparisons\n\n - Develop FastAPI backend services and REST APIs that bridge the platform to running clusters\n\n - Build real-time monitoring and debugging tools (streaming logs, step-level metrics, failure analysis)\n\n - Ship product UI in Next.js / React / TypeScript with shadcn, Tailwind, tRPC, and TanStack Query\n\n\nRESEARCH BRIDGE\n\n - Interface with the RL trainer, inference servers, and environment servers running inside our clusters\n\n - Productize new training capabilities (new model architectures, RL algorithms, modes)\n\n\n\n\n\nTECHNICAL REQUIREMENTS\n\nWe're looking for engineers who are fluent across three areas - you don't need to be the world's best at any one, but you should have real depth in all three and a clear point of view on how they connect.\n\n\n\n\nAI \u0026 GPU LANDSCAPE\n\n - Strong working knowledge of the modern AI stack - open model families, finetuning techniques (LoRA, QLoRA, full FT, RLHF/RLAIF), inference engines (vLLM, SGLang, TensorRT-LLM)\n\n - Familiarity with GPU hardware tradeoffs (H100 / H200 / B200, NVLink, interconnects, memory hierarchy) and what they mean for training and inference workloads\n\n - Understanding of distributed training fundamentals (data/tensor/pipeline/expert parallelism, NCCL, multi-node scheduling)\n\n - Awareness of what's happening at the frontier - new models, training methods, infra patterns - and the ability to translate that into product decisions\n   \n   \n\n\nKUBERNETES \u0026 INFRASTRUCTURE\n\n - Strong Kubernetes operations experience - Helm, CRDs, operators, KEDA, gang scheduling, GPU operator\n\n - Comfortable debugging real production clusters (kubectl, pod lifecycle, node issues, networking)\n\n - Cloud platform experience (GCP preferred - GCS, GKE, Cloud Run, Cloud Tasks)\n\n - Infrastructure automation (Helm, Terraform, Ansible) and a GitOps mindset\n\n - Observability: Prometheus, Grafana, Loki, OpenTelemetry, DCGM\n\n - Linux fundamentals: networking, namespaces, performance tuning\n   \n   \n\n\nPROGRAMMING \u0026 PLATFORM\n\n - Strong Python backend development (FastAPI, async, SQLAlchemy)\n\n - Comfortable building Python contr","salary_min":150000,"salary_max":300000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","llm","gpu","api-design","fine-tuning","distributed-systems","agents","cloud"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/8706578d-5a01-4270-9d43-ed9cd998a982/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:45:47.645Z","expires_at":"2026-08-15T14:10:47.612821Z","created_at":"2026-05-11T14:11:38.576943Z","updated_at":"2026-07-16T14:10:47.733682Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ffb8f345-cc3f-4a19-b74d-6117413ea12c"},{"id":"2d9fb70b-e1df-4ef9-b1ba-f021f1b7f44a","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Research Engineer - Reinforcement Learning","slug":"research-engineer-reinforcement-learning-6acec267","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\n\nRESPONSIBILITIES\n\n - Lead and participate in novel research to build a massive scale synthetic data generation pipeline and orchestration solution\n\n - Optimize the performance, cost, and resource utilization of AI inference workloads by leveraging the most recent advances for compute \u0026 memory optimization techniques.\n\n - Contribute to the development of our open-source libraries and frameworks for synthetic data generation and distributed RL frameworks.\n\n - Publish research in top-tier AI conferences such as ICML \u0026 NeurIPS.\n\n - Distill highly technical project outcomes in layman approachable technical blogs to our customers and developers.\n\n - Stay up-to-date with the latest advancements in AI/ML infrastructure and tools, synthetic data gen research and proactively identify opportunities to enhance our platform's capabilities and user experience.\n\n\nREQUIREMENTS\n\n - Strong background in AI/ML engineering, with extensive experience in designing and implementing end-to-end pipelines for the inference or training of large-scale AI models.\n\n - Deep expertise in distributed inference techniques and frameworks (e.g. vllm, sglang) for optimizing the performance and scalability of AI workloads.\n\n - Solid understanding of MLOps best practices, including model versioning, experiment tracking, and continuous integration/deployment (CI/CD) pipelines.\n\n - Passion for advancing the state-of-the-art in reasoning and democratizing access to AI capabilities for researchers, developers, and businesses worldwide.\n\n - If you're not familiar with these, but feel like that you can contribute to our mission and you're a high-energy person, get familiar with these resources (here https://a.co/d/frW8MHY, here https://a.co/d/4WRhR0Y and here https://github.com/stas00/ml-engineering/tree/master) and please reach out!\n\n\nBENEFITS \u0026 PERKS\n\n - Cash Compensation Range of $150-350k, including equity incentives, aligning your success with the growth and impact of Prime Intellect.\n\n - Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.\n\n - Visa sponsorship and relocation assistance for international candidates.\n\n - Quarterly team off-sites, hackathons, conferences and learning opportunities.\n\n - Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.\n\n\n\nIf you're excited about the opportunity to build the foundation for the future of decentralized AI and create a platform that empowers developers and researchers to push the boundaries of what's possible, we'd love to hear from you.","salary_min":150000,"salary_max":350000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["search","llm","agents","mlops","reinforcement-learning","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/ee13090e-3fea-40f0-b785-19316f52bf08/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:43:44.607Z","expires_at":"2026-08-15T14:10:46.569142Z","created_at":"2026-04-13T15:01:32.560515Z","updated_at":"2026-07-16T14:10:46.797256Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2d9fb70b-e1df-4ef9-b1ba-f021f1b7f44a"},{"id":"b4864caf-5f0c-491e-bf2c-b88fbeea047b","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Applied Research - RL \u0026 Agents","slug":"applied-research-rl-agents-13893686","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\nROLE IMPACT\n\nThis is a role at the intersection of cutting-edge RL/post-training methods and applied agent systems. You’ll have a direct impact on shaping how advanced models are aligned, deployed, and used in the real world by:\n\n - Advancing Agent Capabilities: Designing and iterating on next-generation AI agents that tackle real workloads—workflow automation, reasoning-intensive tasks, and decision-making at scale.\n\n - Building Robust Infrastructure: Developing the systems and frameworks that enable these agents to operate reliably, efficiently, and at massive scale.\n\n - Bridge Between Applications \u0026 Research: Translate ambiguous objectives into clear technical requirements that guide product and research priorities.\n\n - Prototype in the Field: Rapidly design and deploy agents, evals, and harnesses for real-world tasks to validate solutions.\n\n\n\n\nApplication-Driven Research \u0026 Infrastructure\n\n - Shape the direction and feature set for verifiers, the Environments Hub, training services, and other research platform offerings.\n\n - Build high‑quality examples, reference implementations, and “recipes” that make it easy for others to extend the stack.\n\n - Prototype agents and eval harnesses tailored to real-world use cases and external systems.\n\n - Pair with technical end‑users (research teams, infra‑heavy customers, open‑source contributors) to design environments, evals, and verifiers that reflect real workloads.\n\n\n\nPost-training \u0026 Reinforcement Learning\n\n - Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks.\n\n - Build evaluations and harnesses and to measure reasoning, robustness, and agentic behavior in real-world workflows.\n\n - Prototype multi-agent and memory-augmented systems to expand capabilities for downstream applications.\n\n - Experiment with post-training recipes to optimize downstream performance.\n\n\n\nAgent Development \u0026 Infrastructure\n\n - Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making.\n\n - Extend and integrate with agent frameworks to support evolving feature requests and performance requirements.\n\n - Architect and maintain distributed training/inference pipelines, ensuring scalability and cost efficiency.\n\n - Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments.\n\n\n\n\nREQUIREMENTS\n\n - Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment.\n\n - Experience with agent frameworks and tooling (e.g. DSPy, LangGraph, MCP, Stagehand).\n\n - Familiarity with distributed training/inference frameworks (e.g., vLLM, sglang, Accelerate, Ray, Torch).\n\n - Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL.\n\n - Passion for advancing the state-of-the-art in reasoning and building practical, agentic AI systems.\n\n - Strong technical writing abilities (documentation, blogs, papers) and research taste.\n\n - Eagerness to drive collaborations with external partners and engage with the broader open-source community.\n\n\n\n\nNICE-TO-HAVES\n\n - Experience with web programming (React, TypeScript, Next.js).\n\n - Experience running LLM evaluations and/or synthetic data generation.\n\n - Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform).\n   \n\n\nWHAT WE OFFER\n\n - Cash Compensat","salary_min":150000,"salary_max":300000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","distributed-systems","reinforcement-learning","llm","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/46d9d060-5f48-4491-848f-bafbeb3a4325/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:34:52.078Z","expires_at":"2026-08-15T14:10:47.454136Z","created_at":"2026-04-13T15:01:32.590705Z","updated_at":"2026-07-16T14:10:47.573529Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b4864caf-5f0c-491e-bf2c-b88fbeea047b"},{"id":"e4025ddd-fba3-4d70-863f-4b95346b9c5c","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Applied Research - Forward-Deployed","slug":"applied-research-forward-deployed-a5dcad80","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\nABOUT THE ROLE\n\nWe're looking for a Forward-Deployed Research Engineer (FDRE) to serve as the primary technical interface between Prime Intellect and our most important customers: AI companies, research labs, and enterprises running post-training and agentic RL on our platform.\n\nThis is not a traditional research role. You'll spend most of your time embedded with customers, understanding their models, workflows, and goals. Then, you'll translate those objectives into concrete training runs, environment designs, evaluation harnesses, and deployment recipes using the Lab stack. You are the person who makes the platform work in practice for real workloads.\n\nYou'll work closely with our research, product, and infrastructure teams to feed field insights back into the platform, shaping what we build next based on what customers actually need.\n\n\n\n\n\nWHAT YOU'LL DO\n\n\nCUSTOMER ENGAGEMENT \u0026 TECHNICAL DELIVERY\n\n - Embed directly with strategic customers to understand their agent architectures, failure modes, and product goals\n\n - Design and build custom RL environments, evaluation harnesses, and verifiers that capture what \"good\" looks like for each customer's domain\n\n - Architect agent scaffolding — tool use, multi-step reasoning, memory, sandbox execution — tailored to customer workflows\n\n - Configure and launch training runs on Lab, iterating on reward functions, rollout strategies, and evaluation criteria\n\n - Serve as the technical lead for engagements end-to-end: from discovery through deployed, improved models\n\n\nPLATFORM FEEDBACK \u0026 ECOSYSTEM\n\n - Identify repeatable patterns from customer engagements and codify them into reference implementations, templates, and documentation\n\n - Serve as the voice of the customer internally, shaping the roadmap for Lab, verifiers, the Environments Hub, and training infrastructure\n\n - Build high-quality examples and \"recipes\" that make it easy for new customers and open-source contributors to extend the stack\n\n - Contribute to technical content (blog posts, tutorials, case studies) that demonstrates real-world platform usage\n\n\nAPPLIED RESEARCH \u0026 EXPERIMENTATION\n\n - Develop novel evaluation methodologies for agentic behavior — multi-step reasoning, tool use correctness, recovery from failure, long-horizon task completion\n\n - Prototype and iterate on agent harnesses for real-world tasks: code generation, workflow automation, document processing, and more\n\n - Experiment with reward design, rubric construction, and environment shaping to improve training signal quality\n\n - Stay current on the frontier of agentic AI, evals, and post-training methods, and bring that knowledge directly into customer work\n\n\nWHAT WE'RE LOOKING FOR\n\n - Deep hands-on experience building, evaluating, or deploying LLM-based agents in the past 1–2 years — you've seen what breaks in production and know what good evals look like\n\n - Strong intuition for evaluation design: you can look at a customer's agent and quickly identify what to measure, how to construct a rubric, and where the reward signal is weak\n\n - Working understanding of RL and post-training concepts (GRPO, RLHF, reward modeling, SFT) — you don't need to have written a trainer from scratch, but you should understand what the knobs do and why they matter\n\n - Strong Python skills and comfort with the modern AI stack (Hugging Face, inference engines, agent frameworks)\n\n - Experience in a customer-facing or consulting-adjacent technical role, or as","salary_min":150000,"salary_max":300000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["code-generation","agents","reinforcement-learning","search","llm","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/73f42d73-f967-4082-b599-b8914135a6b3/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:34:29.004Z","expires_at":"2026-08-15T14:10:47.843546Z","created_at":"2026-04-13T15:01:32.600939Z","updated_at":"2026-07-16T14:10:47.961822Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e4025ddd-fba3-4d70-863f-4b95346b9c5c"},{"id":"9a7b6e3f-9ca5-4d74-8149-e52a00eeffdc","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Applied Research - Evals \u0026 Data","slug":"applied-research-evals-data-2b9e0702","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\nRole Impact\n\nThis is a customer facing role at the intersection of cutting-edge RL/post-training methods, applied data, and agent systems. You’ll have a direct impact on shaping how advanced models are aligned, evaluated, deployed, and used in the real world by:\n\n - Advancing Agent Capabilities: Designing and iterating on next-generation AI agents that tackle real workloads—workflow automation, reasoning-intensive tasks, and decision-making at scale. Working with applied data from real deployments to continuously refine policies, improve reasoning, and enhance reliability and safety.\n\n - Building Robust Infrastructure: Developing the distributed systems, evaluation pipelines, and coordination frameworks that enable these agents to operate reliably, efficiently, and at massive scale. Building data capture, processing, and versioning workflows for feedback, model traces, and reward signals.\n\n - Bridge Between Customers \u0026 Research: Translating customer needs and insights from applied data into clear technical requirements that guide product and research priorities. Collaborating closely with RL and eval teams to ensure real-world signals inform model alignment and reward shaping.\n\n - Prototype in the Field: Rapidly designing and deploying agents, evals, and harnesses alongside customers to validate solutions. Using applied evaluation data to iterate on model performance and discover new capabilities.\n\n\nCustomer-Facing Engineering\n\n - Work side-by-side with customers to deeply understand workflows, data sources, and bottlenecks.\n\n - Prototype agents, data pipelines, and eval harnesses tailored to real use cases, then hand off hardened systems to core teams.\n\n - Translate customer insights and evaluation results into roadmap and research direction.\n\n\nPost-training \u0026 Reinforcement Learning\n\n - Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks.\n\n - Build evaluation harnesses and verifiers to measure reasoning, robustness, and agentic behavior in real-world workflows.\n\n - Integrate applied data collection and analytics into the post-training process to surface regressions, emergent skills, and alignment opportunities.\n\n - Prototype multi-agent and memory-augmented systems to expand capabilities for customer-facing solutions.\n\n\nAgent Development \u0026 Infrastructure\n\n - Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making.\n\n - Extend and integrate with agent frameworks to support evolving feature requests and performance requirements.\n\n - Architect and maintain distributed training and inference pipelines, ensuring scalability and cost efficiency.\n\n - Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments.\n\n\nRequirements\n\n - Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment.\n\n - Experience with applied data workflows and evaluation frameworks for large models or agents (e.g., SWE-Bench, HELM, EvalFlow, internal eval pipelines).\n\n - Deep expertise in distributed training/inference frameworks (e.g., vLLM, sglang, Ray, Accelerate).\n\n - Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform).\n\n - Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL.\n\n - Passion for advancing the ","salary_min":150000,"salary_max":300000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","agents","data-pipeline","distributed-systems","llm","research","evaluation"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/bbfe94a6-d1a8-47e9-86af-f117277cdacb/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:34:09.743Z","expires_at":"2026-08-15T14:10:47.08875Z","created_at":"2026-04-13T15:01:32.581029Z","updated_at":"2026-07-16T14:10:47.245952Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9a7b6e3f-9ca5-4d74-8149-e52a00eeffdc"},{"id":"5b4c2841-d819-4ab1-87e4-988c9bff0235","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Software Engineer, Identity","slug":"senior-software-engineer-identity-542360e2","description":"Software is eating the world, but AI is eating software. We live in unprecedented times – AI has the potential to exponentially augment human intelligence. Every person will have a personal tutor, coach, assistant, personal shopper, travel guide, and therapist throughout life. As the world adjusts to this new reality, leading platform companies are scrambling to build LLMs at billion scale, while large enterprises figure out how to add it to their products. To make them safe, aligned and actually useful, these models need human eval and reinforcement learning through human feedback (RLHF) during pre-training, fine-tuning, and production evaluations. This is the main innovation that’s enabled ChatGPT to get such a large headstart among competition.\n At Scale, our products include the Generative AI Data Engine, SGP, Donovan, and others that power the most advanced LLMs and generative models in the world through world-class RLHF, human data generation, model evaluation, safety, and alignment. The data we are producing is some of the most important work for how humanity will interact with AI.\n At the foundation of these products is the Identity  Engineering team.  In this role, you will help support the design and development of core software systems specifically focused on identity, access management, authorization, and authentication.  You’ll also get widespread exposure to the forefront of the AI race as Scale sees it in enterprises, startups, governments, and large tech companies.\n You will:\n \n Drive the design, and implementation of our identity infrastructure to ensure secure authentication and authorization across enterprise systems.\n Build software for authentication mechanisms such as Single Sign-On (SSO), Multi-Factor Authentication (MFA), and federated identity solutions (SAML, OAuth, OpenID Connect).\n Build software for authorization mechanisms such as Relation-based access control (ReBAC), Attribute-based access control (ABAC), Role-based access control (RBAC).\n Build software-defined identity governance policies to ensure compliance with security policies, industry regulations (e.g., NIST, SOC2, ISO 27001), and organizational standards.\n Present technical information to teams and stakeholders, providing guidance and insight on identity management and best practices.\n \n Ideally you’d have:\n \n 5+ years of full-time engineering experience, post-graduation with specialities in infrastructure and identity systems.\n Infrastructure expertise – IAM controls, Infrastructure as Code (Terraform, Pulumi), microservice deployment best practices.\n Hands-on experience working with OpenFGA, Authzed, Cedar, Topaz, or similar authorization frameworks at scale.\n Strong understanding of Zanzibar-based ReBAC models, relationship tuples, and access control evaluation.\n Strong knowledge of authentication standards such as OAuth 2.0, OIDC, SAML, and JWT, as well as industry standard IdP solutions like EntraID, Okta, etc.\n Extensive experience in software development and a deep understanding of distributed systems and public cloud platforms (AWS preferred).\n Show a track record of independent ownership of successful engineering projects.\n Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders.\n \n Nice to haves:\n \n Experience securing API access and implementing access control mechanisms at the application level.\n Multi-cloud infrastructure experience – AWS, Azure, GCP, and more.\n Proficiency in integrating IAM solutions with applications built using frameworks such as Java, Python, Node.js, or .NET.\n Mentorship/leadership experience supporting junior engineers\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seat","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","pre-training","microservices","reinforcement-learning","fine-tuning","generative-ai","llm","cloud"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4711898005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:08:31Z","expires_at":"2026-08-15T14:01:44.011459Z","created_at":"2026-07-09T14:01:29.84661Z","updated_at":"2026-07-16T14:01:44.124451Z","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/5b4c2841-d819-4ab1-87e4-988c9bff0235"},{"id":"c25a480b-8502-4bae-9574-8f1650795f9e","company_id":"d5938b59-6207-428e-9457-222629512c5a","title":"Engineering Manager, AI","slug":"engineering-manager-ai-992395cc","description":"This is Engineering at Lattice \n Lattice's Engineering team is dedicated to building cutting-edge solutions that empower people and organizations to thrive. As AI becomes fundamental to every product experience, ensuring those systems are trustworthy, measurable, and continuously improving is one of the company's most important engineering challenges.\n The AI Intelligence Quality (IQ) team owns that challenge. We build the AI Platform that enables every AI team at Lattice to measure quality, detect regressions, run experiments, and continuously improve AI performance with confidence. The platform you lead won't power a single product—it will shape the quality of every AI experience we deliver, becoming a foundational capability for how AI is built at Lattice.\n What You Will Do \n \n Lead, coach, and grow a high-performing team of AI and software engineers, developing them into strong technical leaders while fostering a culture of ownership, technical excellence, experimentation, and continuous learning.\n Own the execution and delivery of the AI Platform roadmap, balancing near-term product impact with long-term platform investments.\n Partner closely with Product, Applied AI, Data Science, and engineering leaders to define how AI quality is measured, evaluated, and continuously improved across Lattice.\n Lead the development of AI evaluation infrastructure, quality metrics, experimentation capabilities, observability, and developer tooling that enable every AI product team to confidently build, evaluate, and ship AI experiences.\n Drive technical and organizational decisions that improve the scalability, reliability, and adoption of the AI Platform, empowering engineers to own architecture and technical solutions.\n Build strong cross-functional partnerships and influence engineering strategy to establish AI evaluations as a foundational capability that accelerates AI development across Lattice.\n \n What You Will Bring to the Table \n \n Experience leading and growing high-performing software engineering teams, with a track record of hiring, coaching, and developing engineers into strong technical leaders.\n Strong technical background leading teams that build AI infrastructure, AI-powered products leveraging Large Language Models (LLMs) and/or AI agents, distributed systems, or developer platforms. You are a highly technical engineering leader who leads by example—contributing to architecture, design reviews, and code reviews, diving into implementation details when needed, and raising the technical bar across the team.\n Experience building enterprise SaaS AI products where quality, trust, reliability, and scalability are critical to customer success.\n Proven ability to lead complex cross-functional initiatives from strategy through execution, partnering effectively with Product, Applied AI, Data Science, and Engineering to deliver business impact.\n Excellent communication and stakeholder management skills, with the ability to create clarity, align teams, and influence technical direction in fast-moving, ambiguous environments.\n A strong product and platform mindset, balancing customer impact, engineering velocity, operational excellence, and long-term technical investments.\n A leadership style grounded in curiosity, humility, ownership, and continuous learning, with a passion for building exceptional engineering teams and delivering high-quality AI products.\n \n Nice to Have \n \n Experience building AI evaluation frameworks, experimentation platforms, or ML infrastructure.\n Knowledge of statistical experimentation, A/B model testing, offline evaluations, or model benchmarking.\n Familiarity with advanced AI optimization techniques such as Direct Preference Optimization (DPO), Reinforcement Learning from Human Feedback (RLHF), model fine-tuning, or preference learning.\n Experience building internal platforms and services adopted across multiple engineering teams.\n \n #LI-remote\n The estimated annual cash salary for this role is $187,500 - $234,500. This position is also eligible for incentive stock options, subject to the terms of Lattice’s applicable plans. \n Benefits: The Company offers the following benefits for this position, subject to applicable eligibility requirements: Medical insurance; Dental insurance; Vision insurance; Life, AD\u0026D, and Disability Insurance; Emergency Weather Support; Wellness Apps; Paid Parental Leave, Paid Time off inclusive of holidays and sick time; Commuter \u0026 Parking Accounts; Lunches in the Office; Internet and Phone Stipend; One time WFH Office Set-Up Stipend; 401(k) retirement plan; Financial Planning; and Learning \u0026 Development Budget. \n Note on Pay Transparency: Lattice provides an estimate of the compensation for roles that may be hired as required by regulations. Compensation may vary based on (a) location, as Lattice factors in specific location when benchmarking compensation for most roles; (b) individual candidate skills and qualifications; and (c) individual candidate exp","salary_min":187500,"salary_max":234500,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","fine-tuning","distributed-systems","agents","llm"],"apply_url":"https://lattice.com/job?gh_jid=8614333002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-07T21:39:43Z","expires_at":"2026-08-15T14:19:42.660089Z","created_at":"2026-07-09T14:18:26.110215Z","updated_at":"2026-07-16T14:19:42.780472Z","company_name":"Lattice","company_slug":"lattice","company_logo_url":"https://www.google.com/s2/favicons?domain=lattice.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c25a480b-8502-4bae-9574-8f1650795f9e"},{"id":"9bf087cc-bea6-427a-bbb1-aa8de95d7ad9","company_id":"aa372131-86ce-432a-af45-e2b42a79ba29","title":"Research Engineers, Post-Training","slug":"research-engineers-post-training-1fd6b6d1","description":"ABOUT DISTYL AI\n\nDistyl is an applied AI technology company partnering with the world’s most ambitious institutions to rearchitect critical operations for the frontier of AI. Our customers include the largest companies in telecom, healthcare, insurance, manufacturing, consumer goods, and global social organizations.\n\nWe research and deploy technologies that power AI-native operations — both for our partners and for Distyl itself. Our work spans research into self-constructing systems, the development of the most reliable execution of AI systems, and products that transform mission-critical workflows. As a result, Distyl's technologies affect some of the world's largest operations — from hundreds of millions of consumer interactions to tens of millions of supply chain transactions and millions of patient journeys.\n\nDistyl is backed by leading investors including Lightspeed Venture Partners, Khosla Ventures, Coatue, DST Global, and the board-members of 20+ F500s. The results reflect this approach: a 100% production deployment success rate for our customers and one of the few enterprise AI companies to run a profitable business.\n\n\n\n\nWHAT WE ARE LOOKING FOR\n\nAt Distyl, Research Engineers build the bridge between frontier AI research and production systems that deliver real business value. This role is for engineers who are excited to investigate how AI systems should be designed, rapidly prototype new ideas, and turn promising concepts into reliable systems that work inside real customer environments.\n\n\n\nResearch Engineers operate at the intersection of applied research, systems engineering, and customer-facing deployment. They design and implement compound AI systems, run experiments to understand system behavior, build evaluation frameworks, and collaborate closely with AI Researchers, AI Engineers, and customer stakeholders. Their work is not limited to demos or isolated prototypes: they help turn new techniques into robust systems that can be measured, operated, and improved in production.\n\n\n\n\nKEY RESPONSIBILITIES\n\n - Design and run post-training workflows that improve the behavior, reliability, and usefulness of AI systems\n\n - Develop datasets, preference signals, evaluation suites, reward models, fine-tuning workflows, and feedback loops for applied AI use cases\n\n - Investigate how different post-training techniques affect system behavior across enterprise workflows and production constraints\n\n - Build infrastructure for experimentation, model comparison, regression testing, and behavior analysis\n\n - Partner with AI Researchers to explore new post-training methods and with AI Engineers to apply successful techniques in deployed systems\n\n - Analyze model outputs, failure modes, human feedback, and production traces to identify opportunities for behavioral improvement\n\n - Create repeatable processes for adapting AI systems to customer domains while preserving robustness, transparency, and maintainability\n\n - Communicate clearly with internal teams and customer stakeholders about model behavior, evaluation results, limitations, and tradeoffs\n\n\n\n\nWHO YOU ARE\n\n - Experience Improving Model Behavior: You have worked with fine-tuning, preference optimization, reinforcement learning, reward modeling, synthetic data, evals, or related post-training techniques\n\n - Strong Programming and Experimentation Skills: You can build training and evaluation pipelines, run controlled experiments, analyze results, and iterate quickly\n\n - Research-Oriented Builder: You care about understanding why behavior changes, not just whether a benchmark improves\n\n - AI Systems Mindset: You understand that model behavior is shaped by data, prompts, tools, retrieval, evaluators, and deployment context—not model weights alone\n\n - AI-Native Working Style: You use AI tools daily to accelerate coding, analysis, debugging, experimentation, and research exploration\n\n - Bias Towards Measurement: You make behavioral improvements concrete through evaluations, comparisons, regression tests, and production-relevant metrics\n\n - Comfort with Applied Constraints: You can balance research ambition with practical constraints around cost, latency, reliability, data availability, and customer requirements\n\n - Ownership Mentality: You take responsibility for whether post-training work improves real system outcomes, not just offline scores\n\n\n\n\nWHAT WE OFFER\n\n - The base salary range for this role is $150K – $250K, depending on experience, location, and level. In addition to base compensation, this role is eligible for meaningful equity, along with a comprehensive benefits package\n\n - 100% coverage of medical, dental, and vision insurance for employee and dependents\n\n - Flexible time off\n\n - Retirement and financial planning benefits, including access to pre-tax HSA, FSA, and commuter accounts, 401(k), and financial coaching resources\n\n - Comprehensive wellness benefits, including physical fitness, mental well-being, and fertility and family-building","salary_min":150000,"salary_max":250000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["healthcare","reinforcement-learning","search","fine-tuning","research"],"apply_url":"https://jobs.ashbyhq.com/distyl/96951117-efef-4f27-bbc4-671559d4af30/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-07T15:39:17.759Z","expires_at":"2026-08-15T14:19:19.695276Z","created_at":"2026-06-28T14:17:24.151827Z","updated_at":"2026-07-16T14:19:19.829698Z","company_name":"Distyl AI","company_slug":"distyl-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=distyl.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9bf087cc-bea6-427a-bbb1-aa8de95d7ad9"},{"id":"6e73bc75-a490-4b93-af2d-5d0040a7eb71","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Research Engineer, Post-Training Inference","slug":"research-engineer-post-training-inference-ff4ae18b","description":"About the role \n The Model Shaping team at Together AI works on products and research focused on tailoring open foundation models to downstream applications. We build services that enable machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad range of ideas across machine learning, natural language processing, and ML systems.\n As a Research Engineer within Model Shaping, you will develop a platform that enables users to customize open-source models with their own data. Working across the training and inference stacks, you will build and improve our Fine-Tuning, Reinforcement Learning, and Evaluation services – from ensuring a seamless path from post-training to production serving, to optimizing the inference engine for RL training workloads. You will collaborate closely with our product, research, and engineering teams to keep the API reliable, performant, and well integrated into the company's technical infrastructure. Above all, you will help build the foundational layer of the open-source AI ecosystem, enabling developers around the world to efficiently create high-quality models tailored to their specific applications.\n Responsibilities \n \n Design and build Together’s systems for customizing open-source models\n Build integrations between the Model Shaping and Inference platforms to ensure a seamless path from post-training to serving production workloads\n Add features to inference engines for large-scale post-training experiments, including optimizations for RL workloads\n Make sure the service is stable and robust, participating in an on-call rotation and ensuring 24/7 availability of our platform\n \n Requirements \n \n Have 2+ years of experience building and deploying machine learning-based services in a production environment\n Have hands-on experience with modern inference engines, such as SGLang, vLLM, and TensorRT-LLM\n Are familiar with the latest methods for fine-tuning LLMs and other AI models\n Have a strong software engineering background in Python or Go\n Stay up to date with the latest advances and trends in the machine learning community\n \n Experience in any of the following will make you stand out \n \n Serving low-precision (FP4/FP8) models, multiple LoRA adapters within one model instance (Multi-LoRA), or models distributed across several GPU nodes\n Optimizing the performance of RL training workloads\n Developing CUDA/Triton/CuTE DSL kernels for inference\n Developing large-scale and high-load production systems\n Maintaining or contributing to open-source ML projects\n Managing machine learning workloads on Kubernetes clusters\n \n About Together AI \n Together AI is a research-driven artificial intelligence company. We believe open and transparent AI systems will drive innovation and create the best outcomes for society, and together we are on a mission to significantly lower the cost of modern AI systems by co-designing software, hardware, algorithms, and models. We have contributed to leading open-source research, models, and datasets to advance the frontier of AI, and our team has been behind technological advancement such as FlashAttention, ATLAS, RedPajama, and Mamba. We invite you to join a passionate group of researchers in our journey in building the next generation AI infrastructure.\n Compensation \n We offer competitive compensation, startup equity, health insurance, and other benefits. The US base salary range for this full-time position is $200,000 - $290,000. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.\n Equal Opportunity \n Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.\n Please see our privacy policy at  https://www.together.ai/privacy","salary_min":200000,"salary_max":290000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["search","llm","nlp","gpu","reinforcement-learning","fine-tuning","generative-ai","research"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5179372007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-06T18:21:40Z","expires_at":"2026-08-15T14:02:19.341947Z","created_at":"2026-07-09T14:02:08.323229Z","updated_at":"2026-07-16T14:02:19.456183Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6e73bc75-a490-4b93-af2d-5d0040a7eb71"}],"market_demand_pack":{"amount_cents":2900,"api_checkout_url":"https://aidevboard.com/api/v1/checkout?product_id=aidevboard_ai_skills_demand_pack","checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","currency":"USD","description":"Full ranked public AI/ML demand CSV, source job URLs, and decision brief with market and offer angles.","fulfillment":"automatic_email_after_paid_checkout","human_checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","name":"AI Market Demand Pack","next_step":"Open checkout_url for Stripe Checkout, or call api_checkout_url to get the non-charging checkout handoff payload.","price_usd":29,"product_id":"aidevboard_ai_skills_demand_pack","quote_url":"https://aidevboard.com/api/v1/quote?product_id=aidevboard_ai_skills_demand_pack"},"page":1,"per_page":20,"total":541,"total_pages":28}