{"access":{"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","employer_pilot_url":"https://aidevboard.com/verified-interview-pilot","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"candidate_resume_action":{"application_authorized":false,"candidate_charge":0,"endpoint":"https://aidevboard.com/api/v1/candidate/resume-preview","job_id_json_path":"jobs[].id","method":"POST","preview_requires_identity":false,"required_body_fields":["job_id","evidence_bullets"],"requires_explicit_human_review":true,"saved_artifact_protocol":"mcp","saved_artifact_requires_verified_human":true,"saved_artifact_tool":"compile_job_specific_resume","search_requires_identity":false,"status":"available_after_candidate_selects_job","submission_performed":false,"uses_candidate_verified_evidence":true},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"6e489569-b334-49ce-8208-039de04ea5ce","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Research Scientist, RL for Autonomous Planning \u0026 World Modeling  ","slug":"research-scientist-rl-for-autonomous-planning-world-modeling-adc21b92","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. \n In this hybrid role, you will report to a Principal Scientist.\n You will: \n \n Participate in Waymo’s Foundation World Model post-training and evaluation\n Research and develop cutting edge RL and Distillation techniques for Autonomous Vehicle Trajectory Planning\n Integrate emerging research from the broader AI community into Waymo’s internal RL infrastructure, conducting rigorous ablations to identify and scale the most promising methods\n Partner with engineering and research teams across Waymo to share recipes, techniques, and post-training best practices to accelerate our collective know-how\n \n You have: \n \n PhD or Masters in Computer Science, Machine Learning, Robotics, or a similar technical field; with 3+ years of industry or post-doc research experience in Reinforcement Learning or Foundation Models\n Demonstration of original contributions to the field through high-impact publications (ArXiv, peer-reviewed conferences like NeurIPS/ICLR/CVPR), technical blog posts, or significant open-source contributions\n Proficiency in implementing model training flows in a scalable, distributed and performant manner such as Data parallel, FSDP and other sharding approaches\n A willingness to work with complexity of globally distributed inference infrastructure\n \n We prefer: \n \n PhD in Computer Science, Machine Learning, or Robotics, with a research focus on Reinforcement Learning, Foundation Models, or Multi-Modal learning\n Extensive experience designing and deploying Reinforcement Learning infrastructure, specifically for on-policy learning or alignment with human preferences\n A consistent history of original contributions to the AI community, evidenced by first-author publications at top-tier venues (e.g., NeurIPS, ICLR, ICRA) or maintaining significant open-source ML projects\n Experience with large scale (many-machine) training infrastructure and techniques for inference with large models such as model sharding/tensor-parallel\n \n In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:\n \n Health, dental, vision, life, disability insurance\n Retirement Benefits: 401(k) with company match\n Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment\n Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary)\n Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks\n Baby Bonding Leave: 18 weeks\n Holidays: 13 paid days per year\n \n Please note that Waymo may not be able to employ remotely in all locations. Please speak with your recruiter about your preferred location for remote work when you begin the interview process\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $204,000 — $259,000 USD","salary_min":204000,"salary_max":259000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","autonomous-vehicles","generative-ai","robotics","research"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8165872","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-30T02:06:16Z","expires_at":"2026-09-29T13:35:05.501784Z","created_at":"2026-08-30T13:35:05.638136Z","updated_at":"2026-08-30T13:35:05.638136Z","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/6e489569-b334-49ce-8208-039de04ea5ce"},{"id":"bd52fe7c-5d97-4e82-bec6-4431216e869e","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Senior/Staff FDE - Synthetic Data Generation","slug":"seniorstaff-fde-synthetic-data-generation-9477fabd","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n \n About the Role\n Snorkel AI is hiring a Forward Deployed Engineer focused on Synthetic Data Generation to partner with leading AI labs and enterprises on their most critical AI initiatives.\n In this role, you will lead the technical execution of complex customer engagements where synthetic data is used to improve model training, evaluation, and performance. You will translate ambiguous model and data challenges into effective data strategies, build scalable generation and evaluation pipelines, and use experimentation to continuously improve data quality and downstream model outcomes.\n You will work across the full delivery lifecycle—from technical discovery and solution design through implementation, evaluation, and production delivery. You will also identify patterns across engagements and turn successful approaches into reusable capabilities, technical standards, and product improvements.\n Main Responsibilities\n Synthetic Data Generation \u0026 Evaluation\n \n Design and build scalable synthetic data generation, transformation, filtering, and evaluation pipelines for complex AI use cases\n Translate model objectives, failure modes, and data gaps into synthetic data strategies, experiments, and technical specifications\n Develop LLM- and ML-assisted workflows to generate high-quality training and evaluation datasets across targeted behaviors, domains, and edge cases\n Build automated evaluators, quality checks, and measurement frameworks to assess correctness, relevance, diversity, coverage, and adherence to customer requirements\n Design and run experiments to measure the impact of synthetic data on downstream model performance and iteratively improve generation approaches\n Package and deliver production-grade datasets with standardized formats, quality assurance, and clear documentation\n \n Forward Deployed Engineering \u0026 Customer Partnership\n \n Lead technical workstreams from initial solution design through production delivery, navigating ambiguity and making sound technical decisions\n Build, refine, and iterate on solutions that address customer needs, incorporating feedback to ensure the delivered work provides tangible value\n Rapidly prototype and productionize solutions across models, data pipelines, APIs, and custom applications\n Communicate technical tradeoffs, experimental results, and recommendations clearly to technical and cross-functional stakeholders\n Serve as a trusted technical partner to customers and internal delivery teams, resolving complex blockers and driving alignment\n \n Technical Leadership \u0026 Scale\n \n Identify recurring patterns across customer engagements and turn successful solutions into reusable pipelines, evaluators, tooling, and best practices\n Define and improve technical standards for synthetic data generation, experimentation, evaluation, and delivery\n Partner with DaaS Engineering and Product teams to influence platform and product capabilities based on real-world customer needs\n Lead technical design reviews, share expertise, and provide guidance to other engineers\n Stay current with emerging synthetic data, LLM evaluation, and data curation techniques and assess their applicability to customer problems\n \n What We're Looking For\n \n 5+ years of experience in machine learning engineering, data science, applied AI, forward deployed engineering, or a similar technical role\n Strong Python skills and experience building reliable production data or ML systems, including containerizing with Docker and deploying on cloud platforms (e.g., AWS, GCP, or Azure)\n Hands-on experience with LLMs—building model-based applications and data workflows with the modern GenAI/LLM stack, and integrating systems, models, and data sources through APIs\n Strong understanding of ML experimentation and evaluation, including defining metrics and using empirical results to guide technical decisions\n Experience building synthetic data, data augmentation, or model-generated training and evaluation datasets\n Experience with LLM evaluation techniques, including LLM-as-a-judge, model-based evaluation, rubric-based evaluation, or custom evaluators\n Demonstrated ability to take ambiguous technical probl","salary_min":180000,"salary_max":320000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","llm","reinforcement-learning","agents","generative-ai","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6167063004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T16:56:44Z","expires_at":"2026-09-29T13:34:01.344588Z","created_at":"2026-08-29T13:34:10.967098Z","updated_at":"2026-08-30T13:34:01.484225Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bd52fe7c-5d97-4e82-bec6-4431216e869e"},{"id":"99ca5606-9232-4b96-8b12-b49baec86bf5","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Senior/Staff FDE - CUA","slug":"seniorstaff-fde-cua-6da3e68e","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n About the Role\n Snorkel AI is hiring a Forward Deployed Engineer focused on Computer Use Agents to partner with leading AI labs and enterprises on their most critical agentic-AI initiatives.\n In this role, you will lead the technical execution of complex customer engagements involving agents that operate computers, browsers, and software environments to complete realistic, multi-step tasks. You will translate ambiguous product and model challenges into robust task environments, datasets, evaluators, and delivery plans that improve agent reliability and downstream performance.\n You will work across the full delivery lifecycle—from technical discovery and solution design through implementation, evaluation, and production delivery. You will also identify patterns across engagements and turn successful approaches into reusable capabilities, technical standards, and product improvements.\n Main Responsibilities\n Computer Use Agents, Data, and Evaluation\n \n Design and build task environments, datasets, and evaluation workflows for computer-using agents operating across browsers, desktop applications, terminals, and other software interfaces\n Translate customer goals, agent failure modes, and real-world workflows into representative, multi-step tasks with clear success criteria\n Develop data-generation, validation, and quality-assurance pipelines for multimodal and agentic training and evaluation data\n Build automated evaluators, checks, and measurement frameworks to assess task completion, correctness, robustness, efficiency, and adherence to requirements\n Diagnose agent failures across planning, tool use, perception, state management, and interaction with user interfaces; turn findings into improved tasks, data, and evaluations\n Design and run experiments to measure how data, task design, and evaluation changes affect downstream agent performance\n Deliver reusable, production-grade task suites, datasets, and evaluation assets that help customers train, benchmark, and improve computer-use agents\n \n Forward Deployed Engineering \u0026 Customer Partnership\n \n Lead technical workstreams from initial solution design through production delivery, navigating ambiguity and making sound technical decisions\n Build, refine, and iterate on solutions that address customer needs, incorporating feedback to ensure the delivered work provides tangible value\n Rapidly prototype and productionize solutions across models, agent frameworks, APIs, browser or desktop environments, and custom applications\n Communicate technical tradeoffs, experimental results, and recommendations clearly to technical and cross-functional stakeholders\n Serve as a trusted technical partner to customers and internal delivery teams, resolving complex blockers and driving alignment\n \n Technical Leadership \u0026 Scale\n \n Identify recurring patterns across customer engagements and turn successful solutions into reusable task frameworks, evaluators, tooling, and best practices\n Define and improve technical standards for agent task design, environment reliability, evaluation, and delivery\n Partner with DaaS Engineering, Research, and Product teams to influence platform and product capabilities based on real-world customer needs\n Lead technical design reviews, share expertise, and provide guidance to other engineers\n Stay current with emerging agentic-AI, computer-use, evaluation, and data-curation techniques and assess their applicability to customer problems\n \n What We're Looking For\n \n 5+ years of experience in machine learning engineering, software engineering, applied AI, forward deployed engineering, solutions engineering, or a similar technical role\n Strong Python skills and experience building reliable production software, data, or ML systems\n Hands-on experience building, evaluating, or deploying LLM-based or agentic systems, including computer-use agents (CUA)\n Strong understanding of experimentation and evaluation, including LLM-as-a-judge / model-based evaluation, defining metrics, and using empirical results to guide technical decisions\n Experience designing task environments, datasets, and verifiers for agents, including reward \u0026 verifier desi","salary_min":180000,"salary_max":320000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","fine-tuning","reinforcement-learning","data-pipeline","agents","llm"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6167049004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T16:38:34Z","expires_at":"2026-09-29T13:34:01.251345Z","created_at":"2026-08-29T13:34:10.876038Z","updated_at":"2026-08-30T13:34:01.388379Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/99ca5606-9232-4b96-8b12-b49baec86bf5"},{"id":"1cab6a2a-2b5f-4e26-b0dd-0a2b833905b0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Software Engineer, RL Data Platform","slug":"staff-software-engineer-rl-data-platform-224ae32b","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 Anthropic's RL Data Platform team builds the systems that produce, move, and serve the human data Claude learns from: the interfaces humans use to give feedback, the pipelines that turn raw feedback into training signal, and the tooling researchers use to launch, monitor, and inspect data collection. Every RL run depends on a steady supply of high-quality data - human feedback, expert demonstrations, graded transcripts - and when a researcher has an idea for new data on Monday, our job is to make it collectable by Wednesday and in the training mix by Friday.\n This is a full-stack, ownership-heavy role on a small, senior team. You'll design and ship web interfaces used by thousands of expert annotators, build the backend services and data pipelines behind them, and work directly with RL researchers to understand what data they need and why. You'll scope your own projects, make architectural calls, and see them through to production. We're looking for engineers who treat researchers as their users, build for reliability first, and care as much about the shape of the data leaving the system as the UI going into it.\n Key responsibilities \n \n \n Design, build, and operate the feedback and data collection interfaces used by human annotators, domain experts, and internal researchers.\n \n Build and maintain the backend services, APIs, and pipelines that route model samples to humans and return structured feedback to training.\n \n Own the reliability, latency, and usability of systems that run continuously against live model endpoints.\n \n Partner with RL researchers to translate loosely specified data needs into well-scoped collection campaigns and the tooling to run them.\n \n Build dashboards, monitoring, and inspection tools so researchers can see data quality and throughput without asking an engineer.\n \n Identify and remove the bottlenecks between \"we want this data\" and \"it's in the training mix\".\n \n Minimum qualifications \n \n \n Strong full-stack engineering skills, with production experience in TypeScript/React on the frontend and Python on the backend.\n \n Experience designing and operating backend services and data pipelines that other teams depend on.\n \n A track record of owning projects end-to-end, from an ambiguous brief to something in production that people use.\n \n Comfort working directly with technical stakeholders whose needs change week to week, and the judgment to push back when something isn't worth building.\n \n Effective use of AI tools in your own day-to-day work.\n \n Care about the societal impacts of your work.\n \n Preferred qualifications \n \n \n Experience building annotation, labelling, evaluation, or other human-in-the-loop data tooling.\n \n Experience with RLHF, preference data, or other human-feedback pipelines for ML systems.\n \n Experience shipping researcher-facing or other expert-facing internal tools people love: interviewing users, hunting down friction, measurably improving the experience.\n \n Experience running experiments on data collection interfaces and using the results to improve data quality.\n \n Experience working with crowdworker or expert vendor platforms at scale.\n \n Familiarity with how LLMs are trained and evaluated.\n \n Representative projects \n \n \n Build an interface that lets a domain expert review a long agentic transcript, flag the step where things went wrong, and write a corrected continuation - with the result landing in a training-ready format.\n \n Rework the sampling path between our feedback interfaces and model endpoints to cut time-to-first-sample for annotators.\n \n Build a campaign launcher that lets a researcher stand up a new data collection effort (task, rubric, population, quality checks) without writing code.\n \n Instrument annotator behaviour to detect low-effort or adversarial work and surface it to the quality team automatically.\n \n Design the data model for a kind of feedback we haven't collected before, and ship the pipeline that gets it into the training mix.\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 $320,000 — $405,000 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","salary_min":320000,"salary_max":405000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","agents","data-pipeline","reinforcement-learning","alignment","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5404730008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T13:21:33Z","expires_at":"2026-09-29T13:30:42.210504Z","created_at":"2026-08-27T13:30:43.573151Z","updated_at":"2026-08-30T13:30:42.358845Z","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/1cab6a2a-2b5f-4e26-b0dd-0a2b833905b0"},{"id":"d9e245d1-a6c7-4071-9742-6a6ed60d8557","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Research Engineer, RL Data Platform","slug":"staff-research-engineer-rl-data-platform-41e9b926","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 Anthropic's RL Data Platform team builds the systems that produce, move, and serve the human data Claude learns from: the interfaces humans use to give feedback, the pipelines that turn raw feedback into training signal, and the tooling researchers use to launch, monitor, and inspect data collection. Every RL run depends on a steady supply of high-quality data - human feedback, expert demonstrations, graded transcripts - and when a researcher has an idea for new data on Monday, our job is to make it collectable by Wednesday and in the training mix by Friday.\n This is a full-stack, ownership-heavy role on a small, senior team. You'll design and ship web interfaces used by thousands of expert annotators, build the backend services and data pipelines behind them, and work directly with RL researchers to understand what data they need and why. You'll scope your own projects, make architectural calls, and see them through to production. We're looking for engineers who treat researchers as their users, build for reliability first, and care as much about the shape of the data leaving the system as the UI going into it.\n Key responsibilities \n \n \n Design, build, and operate the feedback and data collection interfaces used by human annotators, domain experts, and internal researchers.\n \n Build and maintain the backend services, APIs, and pipelines that route model samples to humans and return structured feedback to training.\n \n Own the reliability, latency, and usability of systems that run continuously against live model endpoints.\n \n Partner with RL researchers to translate loosely specified data needs into well-scoped collection campaigns and the tooling to run them.\n \n Build dashboards, monitoring, and inspection tools so researchers can see data quality and throughput without asking an engineer.\n \n Identify and remove the bottlenecks between \"we want this data\" and \"it's in the training mix\".\n \n Minimum qualifications \n \n \n Strong full-stack engineering skills, with production experience in TypeScript/React on the frontend and Python on the backend.\n \n Experience designing and operating backend services and data pipelines that other teams depend on.\n \n A track record of owning projects end-to-end, from an ambiguous brief to something in production that people use.\n \n Comfort working directly with technical stakeholders whose needs change week to week, and the judgment to push back when something isn't worth building.\n \n Effective use of AI tools in your own day-to-day work.\n \n Care about the societal impacts of your work.\n \n Preferred qualifications \n \n \n Experience building annotation, labelling, evaluation, or other human-in-the-loop data tooling.\n \n Experience with RLHF, preference data, or other human-feedback pipelines for ML systems.\n \n Experience shipping researcher-facing or other expert-facing internal tools people love: interviewing users, hunting down friction, measurably improving the experience.\n \n Experience running experiments on data collection interfaces and using the results to improve data quality.\n \n Experience working with crowdworker or expert vendor platforms at scale.\n \n Familiarity with how LLMs are trained and evaluated.\n \n Representative projects \n \n \n Build an interface that lets a domain expert review a long agentic transcript, flag the step where things went wrong, and write a corrected continuation - with the result landing in a training-ready format.\n \n Rework the sampling path between our feedback interfaces and model endpoints to cut time-to-first-sample for annotators.\n \n Build a campaign launcher that lets a researcher stand up a new data collection effort (task, rubric, population, quality checks) without writing code.\n \n Instrument annotator behaviour to detect low-effort or adversarial work and surface it to the quality team automatically.\n \n Design the data model for a kind of feedback we haven't collected before, and ship the pipeline that gets it into the training mix.\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","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","alignment","data-pipeline","agents","search","reinforcement-learning","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5404725008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T13:04:41Z","expires_at":"2026-09-29T13:30:36.347344Z","created_at":"2026-08-27T13:30:36.814345Z","updated_at":"2026-08-30T13:30:36.489598Z","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/d9e245d1-a6c7-4071-9742-6a6ed60d8557"},{"id":"a60887bd-18b6-4819-b8ac-a8ce688f7d3f","company_id":"a0000000-0000-0000-0000-000000000003","title":"Machine Learning Research Scientist, Evaluations","slug":"machine-learning-research-scientist-evaluations-47ca5c35","description":"Scale works with the industry's leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling) and evaluation. This role is on the evaluation pod within the GenAI Research Organization and will focus on building benchmarks and diagnosing model failure modes in both text and multimodal modalities.\n In this role, you will develop rigorous evaluations and diagnostic methods that reveal where frontier models fail and why. You will collaborate with researchers and engineers to define best practices in evaluation-driven AI development. You will also partner with top foundation model labs to translate failure analysis into technical and strategic input on the next generation of generative AI models.\n You will: \n \n Analyze model behavior to identify, characterize, and diagnose failure modes in frontier LLMs and Agents.  You’ll identify everything from capability gaps and reasoning errors to robustness and alignment issues, all focusing on RCA.\n Design and build benchmarks and evaluation methods that measure LLM capabilities in both text and multimodal modalities.\n Apply post-training expertise (SFT, RLHF, reward modeling) to connect observed failures to the data and training interventions that address them.\n Publish research findings in top-tier AI conferences.\n \n Ideally you’d have: \n \n Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.\n Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.\n Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning, and with LLM evaluation or benchmark development.\n Excellent written and verbal communication skills.\n Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals.\n Previous experience in a customer facing role.\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 $180,600 — $225,750 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 working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information. \n We comply with the United States Department of Labor's Pay Transparency provision .  \n PLEASE NOTE: We co","salary_min":180600,"salary_max":225750,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["deep-learning","fine-tuning","generative-ai","reinforcement-learning","search","nlp","llm","evaluation"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4728014005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T18:51:42Z","expires_at":"2026-09-29T13:31:40.355249Z","created_at":"2026-08-27T13:31:38.7307Z","updated_at":"2026-08-30T13:31:40.501539Z","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/a60887bd-18b6-4819-b8ac-a8ce688f7d3f"},{"id":"c176fb2a-4dab-4c84-9b30-9565eee362ea","company_id":"46e2df02-755e-46ce-a584-3f5881f34183","title":"Product Manager, Finance","slug":"product-manager-finance-c4294318","description":"About Turing \n Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com .  \n  \n Position Overview \n We are looking for an experienced Product Manager to lead the strategy, roadmap, and execution for platforms supporting Back Office Operations, especially around Fund controller accounting and Global Business Finance. The ideal candidate brings strong product management experience combined with deep knowledge of financial operations, operational controls, and controller functions.\n This role will partner closely with business stakeholders, engineering, and operations teams to modernize back-office platforms and improve operational efficiency. While familiarity with AI is a plus, the primary focus is on delivering business value through well-designed operational systems rather than building AI solutions.\n Key Responsibilities \n \n Own the product vision, roadmap, and prioritization for Operations \u0026 Controls platforms.\n Lead product discovery by understanding business problems, operational workflows, and stakeholder requirements.\n Partner with Back Office Operations, Controllers, Finance, and Technology teams to improve operational processes.\n Drive modernization initiatives in fund control accounting workflow automation, and operational reporting.\n Build and manage the product backlog, define user stories, and prioritize features based on business value.\n Coordinate delivery across engineering, architecture, and business stakeholders.\n Track milestones, dependencies, risks, and delivery progress.\n Promote transparency and effective stakeholder communication throughout the product lifecycle.\n Identify practical opportunities where AI and automation tools can improve productivity, decision-making, and operational efficiency.\n Ensure solutions meet governance, audit, and operational control requirements.\n \n Required Qualifications \n \n 5+ years of experience as a Product Owner, Product Manager, or Senior Business Analyst within Financial Services.\n Strong experience supporting Back Office Operations, Fund Controllers, and Finance Operations .\n Has fund accounting domain literacy , hands-on exposure to at least one fund accounting/admin platform, exception management\n Experience building or enhancing corporate back-office platforms and operational systems .\n Demonstrated experience translating fund control requirements — NAV integrity, cash/position break resolution, and controller sign-off workflows — into product specs and acceptance criteria that satisfy audit and governance standards.\n Excellent stakeholder management and cross-functional collaboration skills.\n Proven ability to manage product roadmaps, planning, prioritization, and execution.\n Strong analytical, communication, and problem-solving skills.\n Familiarity with AI tools (e.g., ChatGPT, Copilot, Gemini) and understanding of where AI can improve operational workflows. Hands-on AI solution development is not required. \n Ability to work effectively with engineering teams and understand modern software delivery practices.\n \n Preferred Qualifications \n \n Experience with platforms such as Geneva/SS\u0026C, Eagle, Advent, Paxus , or similar fund accounting and operations systems.\n Experience working with fund administrators or investment operations teams.\n Experience with Agile product delivery.\n Exposure to workflow automation, cloud platforms, or modern enterprise applications.\n \n Compensation Range:  $175,000 - $215,000 total cash   #LI-VC1 \n Values \n \n We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. \n We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection \n We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity.\n \n Advantages of joining Turing \n \n Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. \n Contribute to leading-edge AI research and showcase your work at ","salary_min":175000,"salary_max":215000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["code-generation","reinforcement-learning","healthcare","agents"],"apply_url":"https://job-boards.greenhouse.io/turing/jobs/6150811004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T20:29:31Z","expires_at":"2026-09-29T13:35:13.232941Z","created_at":"2026-08-26T13:35:15.599435Z","updated_at":"2026-08-30T13:35:13.370674Z","company_name":"Turing","company_slug":"turing","company_logo_url":"https://www.google.com/s2/favicons?domain=turing.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c176fb2a-4dab-4c84-9b30-9565eee362ea"},{"id":"385357c5-623e-4db0-baf5-06182c0f554f","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Staff Machine Learning Engineer, Emergency Trajectory Models","slug":"staff-machine-learning-engineer-emergency-trajectory-models-f0038f44","description":"About us    \n Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.\n Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n The role  \n As a Staff Machine Learning Engineer in Wayve's AV Core organization, you will lead the technical direction and delivery of a learned emergency trajectory model for low-frequency, high-consequence maneuvers such as evasive steering and emergency braking. You will take the programme from problem definition through modelling, evaluation, integration, and evidence for deployment.\n Emergency maneuvers are rare, high-consequence events that place unusual demands on data, modelling, and validation. The hard problem is not simply to train another trajectory head: it is to define the operating envelope of a specialist model, what evidence shows that it improves outcomes without introducing new failure modes, and how it integrates with the general driving model and surrounding system. You will lead that work across AV Core and with partners across simulation, evaluation, safety, and product engineering.\n  \n Key responsibilities \n \n Set the technical strategy and roadmap for the emergency trajectory model, including its behavioral scope, operating envelope, system interfaces, and measurable acceptance criteria.\n Design and train trajectory-generating policies using the methods best supported by evidence, including behaviour cloning, reinforcement learning, or other sequential decision-making approaches.\n Build a data strategy for rare emergency cases, combining fleet data, targeted mining, simulation, augmentation, and reweighting while controlling coverage gaps and unintended behavior.\n Create rigorous open-loop and closed-loop evaluations for collision avoidance, evasive steering, emergency braking, recovery, robustness, latency, and regressions in nominal driving.\n Lead integration into the shared driving stack, align technical decisions across teams, and raise the bar through architecture reviews, mentoring, and clear communication of risks, trade-offs, and evidence.\n \n About you   \n In order to set you up for success as a Staff Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.  \n  \n Essential  \n \n A track record of staff-level technical leadership: setting direction for ambiguous machine learning programmes, aligning multiple teams, and carrying work from research through production deployment.\n Deep expertise developing learned trajectory-generation or policy models for embodied systems, including architecture design, objective design, training, and empirical validation.\n Hands-on experience with behaviour cloning, reinforcement learning, or related methods, including objective design, distribution shift, robustness, and closed-loop failure analysis.\n Strong machine learning engineering skills in Python and PyTorch, with experience building reproducible training and evaluation systems on large, heterogeneous datasets.\n Exceptional technical judgement and communication: able to make safety-relevant trade-offs explicit, define the evidence needed for decisions, and lead without relying on formal authority.\n \n  \n Desirable  \n \n Experience applying learned models in autonomous driving or robotics, with strong understanding of motion planning, vehicle dynamics, control, or collision avoidance.\n Experience with specialist, fallback, redundant, mixture-of-experts, or model-routing architectures and the interfaces used to select between them.\n Experience mining, generating, or evaluating rare events using simulation and fleet or real-world data.\n Experience deploying learned policies under real-time latency, reliability, and compute constraints; proficiency in C++, CUDA, or systems optimisation.\n Experience with multimodal, transformer-based, diffusion-based, or other generative trajectory or policy models.\n \n This is a full-time role based in our office in Sunnyvale.  At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and lear","salary_min":336400,"salary_max":370300,"location":"Sunnyvale, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","gpu","robotics","autonomous-vehicles","generative-ai","pytorch","machine-learning"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8747065002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T18:15:27Z","expires_at":"2026-09-29T13:43:29.618384Z","created_at":"2026-08-25T18:31:14.484268Z","updated_at":"2026-08-30T13:43:29.754954Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/385357c5-623e-4db0-baf5-06182c0f554f"},{"id":"fd34c611-28f9-40e5-9aeb-f4ba1b279373","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff ML Engineer, Foundation Models","slug":"software-engineer-foundation-models-e3613df0","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.\n This role follows a hybrid work schedule and reports to a Director of AI Foundations. \n You will: \n \n Work on connecting large-scale foundation models with production systems.\n Adapt our foundation models to new sensors, new platforms, and new production requirements.\n Collaborate extensively with other teams to land foundation models to our next-gen platforms.\n \n You have: \n \n Experience in building large-scale systems\n Track record of solving complex system problems\n Track record of successful deliveries through large-scale collaborations\n \n We prefer: \n \n Experience in LLM/VLM related foundation models or systems\n Experience with foundation model post-training\n \n  \n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","reinforcement-learning","autonomous-vehicles","generative-ai","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8152556","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-24T19:15:00Z","expires_at":"2026-09-29T13:35:10.669553Z","created_at":"2026-08-25T18:27:25.923011Z","updated_at":"2026-08-30T13:35:10.807945Z","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/fd34c611-28f9-40e5-9aeb-f4ba1b279373"},{"id":"43981d60-042d-4f1c-983b-58fc257fe83c","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"Senior Director of Engineering, Generative AI ","slug":"senior-director-of-engineering-generative-ai-1c8821c2","description":"Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.  \n At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.  \n A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. \n Senior Director, Generative AI \n About the Role \n Roblox Build is our generative creation product, the platform where creators design, build, and publish 3D experiences. We are looking for a Senior Director of Generative AI to lead the Applied AI organization inside Build, responsible for turning state-of-the-art foundation models into high-quality, reliable creation systems at Roblox scale. This leader will own the full applied AI stack: model strategy and routing, model adaptation and fine-tuning, code generation (CodeGen), 3D layout generation (LayoutGen), and the evaluation science and infrastructure that tells us what actually works. \n You Will \n \n Own model strategy and routing for Build. Design and build an intelligent model layer that selects the right model for each creation task based on quality, capability, latency, cost, and safety, leveraging both frontier models and Roblox-adapted open-source models.\n Lead model adaptation across the Applied AI org, including fine-tuning, distillation, synthetic data generation, human feedback pipelines, and preference optimization for Roblox-specific creation tasks such as Luau code generation and 3D scene understanding.\n Drive CodeGen capabilities forward by building AI systems that understand creator intent, reason across multi-file Roblox experiences, execute tools, and reliably make complex changes to existing games, going well beyond code completion.\n Build LayoutGen intelligence by developing the AI capability that turns a creator's intent into coherent 3D scenes, including object selection, spatial reasoning, placement, aesthetic quality, and iterative editing.\n Own evaluation as a core technical discipline. Build the benchmarks, quality metrics, judge methodologies, experiment infrastructure, and human evaluation pipelines that answer whether model, prompt, or routing changes actually improve creator outcomes.\n Close the feedback loop between model development, evaluation, and production, ensuring that quality signal flows continuously from creator outcomes back into training data, model updates, and release decisions, rather than operating as separate silos.\n \n You Have \n \n 10+ years of experience in machine learning and AI, with 5+ years in senior technical leadership roles overseeing applied AI or ML engineering organizations that have shipped generative AI systems into production at scale.\n Deep technical grounding across multiple areas of applied AI, including LLM post-training (RLHF, DPO, distillation), model routing and adaptation, agentic systems, code generation, or evaluation science, with enough depth to recruit, challenge, and lead exceptional scientists and engineers in each area.\n Demonstrated experience building and leading high-performing organizations that span both AI research scientists and production engineers, including hiring, developing talent, and making difficult personnel decisions.\n Strong evaluation fluency, with a track record of building eval frameworks that predict production outcomes, designing benchmarks that measure what matters, and building the infrastructure to run experiments continuously at model, prompt, and routing level.\n Experience operating cross-functionally in complex technical organizations, with the ability to influence and align across research, product engineering, and platform teams without relying on hierarchy.\n Genuine interest in the creation problem. You find the specific challenge of AI-assisted 3D world creation and domain-specific code generation compelling, not just as a vehicle for building AI systems.\n \n You Are \n \n A builder and a shipper who cares about getting things into production and measuring whether they actually work, not just publishing promising results.\n Technically credible across the stack. You can engage deeply with scientists on evaluation methodology, data quality, and model behavior, and equally deeply with engineers on architecture, inference latency, and reliability.\n Decisive under ambiguity. You make principled tradeoffs between rigor and velocity, know when good enough is good enough, and can articulate your reasoning clearly.\n Direct and honest. You give clear feedback, surface problem","salary_min":525510,"salary_max":573690,"location":"San Mateo, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["reinforcement-learning","generative-ai","fine-tuning","code-generation","llm","agents"],"apply_url":"https://careers.roblox.com/jobs/8131695?gh_jid=8131695","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T20:23:10Z","expires_at":"2026-09-29T13:47:49.503186Z","created_at":"2026-08-25T18:33:06.097534Z","updated_at":"2026-08-30T13:47:49.630588Z","company_name":"Roblox","company_slug":"roblox","company_logo_url":"https://www.google.com/s2/favicons?domain=roblox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/43981d60-042d-4f1c-983b-58fc257fe83c"},{"id":"e7e0e242-22d2-423a-88a2-fe5d88e92962","company_id":"30ae23c9-d589-41d4-be9b-1ff0a3d094bb","title":"Lab Automation - Robotics Engineer","slug":"lab-automation-robotics-engineer-e0fde2b2","description":"About Xaira Therapeutics \n Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London.\n About the Role \n We are building a next-generation lab automation and robotics platform to transform how biological experiments are executed, monitored, and scaled. This platform will bring together robotic arms, mobile platforms, lab instruments, sensors, computer vision, workflow orchestration, and AI-enabled autonomy to increase the speed, reliability, reproducibility, and scale of experimental biology.\n As a Robotics Engineer, Lab Automation, you will help turn real scientific workflows into robust automated systems. You will work closely with biologists, chemists, automation engineers, software engineers, and robotics experts to understand manual laboratory processes and translate them into reliable robotic execution.\n This is a hands-on engineering role for someone who enjoys building, integrating, testing, and debugging physical systems. You will contribute to robotic manipulation, instrument integration, perception, task execution, simulation, and multi-station lab automation workflows. You will also have opportunities to grow into more advanced robotics, autonomy, and AI-enabled laboratory automation over time.\n What You’ll Do \n \n Build, integrate, and maintain robotics software for robotic arms, mobile robots, sensors, grippers, and laboratory automation devices.\n Develop motion planning, manipulation, perception, and safety logic for robotic systems operating in real laboratory environments.\n Integrate laboratory instruments and devices through APIs, IoT interfaces, schedulers, and custom control software.\n Help build task execution frameworks using state machines, behavior trees, planners, and recovery logic for autonomous or semi-autonomous lab workflows.\n Benchmark and integrate computer vision methods for object detection, segmentation, tracking, pose estimation, localization, and scene understanding.\n Create and use simulation or digital twin environments for motion validation, workflow testing, synthetic data generation, and system debugging.\n Collaborate with scientists to translate biological and chemical workflows into automated protocols.\n Support multi-station system integration, automated material handling, sample tracking, validation, logging, and error recovery.\n Work hands-on with physical hardware to debug real systems, improve reliability, and iterate quickly.\n Evaluate modern AI, autonomy, foundation-model, reinforcement-learning, or imitation-learning approaches where they can improve real robotic lab execution.\n \n Qualifications (Required): \n \n Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical Engineering, Mechanical Engineering, or related field. \n Recent graduates with strong robotics, automation, hardware/software integration, or relevant project experience are encouraged to apply.\n PhD candidates are welcome to apply, particularly if they are excited by hands-on robotic system building, integration, and lab automation.\n Strong software engineering skills in one or more modern programming languages (e.g., Python, C++, C#), cloud-based workflows, and production software systems.\n Familiarity with robotics software frameworks such as ROS2, MoveIt, or comparable tools.\n Familiarity with computer vision, perception, motion planning, robotic manipulation, or sensor integration.\n Comfort working with physical hardware, debugging real systems, and learning by doing.\n Strong problem-solving skills, attention to detail, and ability to work across disciplines.\n Ability to communicate clearly with scientists, automation engineers, software engineers, and robotics experts.\n \n Preferred Qualifications: \n \n Experience with 6-axis robotic arms, autonomous mobile robots, grippers, machine vision systems, or automated material handling.\n Experience with OpenCV, object detection, segmentation, tracking, pose estimation, SLAM, localization, or scene understanding.\n Experience with Foxglove, Isaac Sim, MuJoCo, Gazebo, or other robotics simulation and debugging tools.\n Experience integrating laboratory instruments such as liquid handlers, plate hotels, incubators, readers, and microscopes.\n Experience with agentic AI frameworks (LangGraph, OpenAI Agents SDK, AutoGen, PydanticAI), Model Context Protocol (MCP), or AI systems that integrate language models with external tools and software.\n Familiarity wi","salary_min":150000,"salary_max":180000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["reinforcement-learning","gpu","generative-ai","robotics","computer-vision","agents"],"apply_url":"https://job-boards.greenhouse.io/xairatherapeutics/jobs/5212689007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T20:20:49Z","expires_at":"2026-09-29T13:51:03.109807Z","created_at":"2026-08-25T19:52:46.816947Z","updated_at":"2026-08-30T13:51:03.241025Z","company_name":"Xaira Therapeutics","company_slug":"xaira-therapeutics","company_logo_url":"https://www.google.com/s2/favicons?domain=xaira.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e7e0e242-22d2-423a-88a2-fe5d88e92962"},{"id":"d5aae1c9-cae9-47d5-9fce-478d44a8cb20","company_id":"4ed3e523-b627-46ed-8dae-04c2ea823be2","title":"Research Scientist/Research Engineer, Reinforcement Learning ","slug":"research-scientistresearch-engineer-reinforcement-learning-2476fcd9","description":"Jump Trading Group is committed to world-class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting-edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incentivizing collaboration and mutual respect. At Jump, research outcomes drive more than superior risk-adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.\n Our team is a group of quantitative researchers, engineers, and ML experts leading reinforcement learning research and trading at Jump. Our mission is to combine emerging techniques and original research to learn optimal decision-making policies from financial market data and monetize them globally. We are building the future of ML-powered trading through breakthrough reinforcement learning, and we're looking for an exceptional Research Scientist/Research Engineer to join our team.\n What You’ll Do \n As a Research Scientist/Research Engineer working on RL, you'll be at the forefront of applying reinforcement learning to markets. You'll conduct original research and own the systems that turn it into production trading: designing and evaluating policy architectures, reward formulations, and objective horizons with rigorous out-of-sample benchmarking; partnering with trading and research teams to source, integrate, and validate their alpha signals within the RL framework; ensuring simulation fidelity against live trading by modeling market microstructure, fill dynamics, liquidity, and latency; building efficient tooling to store, process, and analyze very large volumes of market and signal data; and communicating findings to technical and trading audiences. This isn't incremental optimization; we're pushing the boundaries of what reinforcement learning can do at scale, where your improvements directly impact live trading.\n Other duties as assigned or needed. Skills You’ll Need \n \n 5+ years of experience developing reinforcement learning and/or deep learning systems with measurable impact in industry and/or academia\n Depth in reinforcement learning, including experience designing reward formulations, policy architectures, and evaluation, and taking RL methods from research into production\n Proficiency in Python and/or C++\n Familiarity with ML libraries/frameworks such as PyTorch (preferred), TensorFlow, and/or JAX\n Strong foundation in mathematics and statistics\n PhD or Master's degree in Computer Science, Machine Learning, Robotics (or a related subject)\n Strong publication record at ICML, ICLR, AAAI, NeurIPS, CVPR, or equivalent\n Ability to thrive in a collaborative, team-oriented environment\n Creative thinkers who are driven, self-motivated, and eager to solve challenging problems\n Reliable and predictable availability\n Excellent written and verbal communication skills in English\n Benefits \n \n Discretionary bonus eligibility \n Medical, dental, and vision insurance \n HSA, FSA, and Dependent Care options \n Employer Paid Group Term Life and AD\u0026D Insurance \n Voluntary Life \u0026 AD\u0026D insurance \n Paid vacation plus paid holidays \n Retirement plan with employer match \n Paid parental leave \n Wellness Programs \n \n Annual Base Salary Range \n $200,000 — $350,000 USD","salary_min":200000,"salary_max":350000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","tensorflow","search","deep-learning","reinforcement-learning","robotics","research"],"apply_url":"https://www.jumptrading.com/hr/job?gh_jid=8122860","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T18:54:10Z","expires_at":"2026-09-29T13:47:31.484855Z","created_at":"2026-08-27T13:48:12.175153Z","updated_at":"2026-08-30T13:47:31.614748Z","company_name":"Jump Trading","company_slug":"jump-trading","company_logo_url":"https://www.google.com/s2/favicons?domain=jumptrading.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d5aae1c9-cae9-47d5-9fce-478d44a8cb20"},{"id":"b04c0a03-fc25-4cc1-85e1-9c6efc357643","company_id":"97187e1c-a220-4e7e-aa1e-cd5342f434c1","title":"Machine Learning Engineer","slug":"machine-learning-engineer-50e35dc4","description":"Who we are \n About Stripe \n Stripe, LLC. is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.\n What you’ll do \n Responsibilities \n \n Design state-of-the-art ML models and large-scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints. \n Design systems to speed up the time from idea to deployment of new models. \n Experiment and iterate on ML models (using tools including PyTorch and TensorFlow) to achieve key business goals and drive efficiency. \n Develop pipelines and automated processes to train and evaluate models in offline and online environments. \n Integrate ML models into production systems and ensure their scalability and reliability. \n Collaborate with product and strategy partners to propose, prioritize, and implement new product features. \n Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions.\n \n Who you are \n Minimum requirements \n Must have a Bachelor's degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of experience in Building and shipping ML systems in production.\n Must have two (2) years of experience in each of the following:\n \n ML algorithms and model architectures;\n Designing, training and evaluating machine learning models;\n Productionizing and deploying machine learning models at scale;\n Orchestrating data pipelines and leveraging large-scale datasets; and\n Building and deploying ML models to solve business problems.\n \n Must have one (1) year of experience in each of the following:\n \n ML libraries and frameworks including PyTorch, TensorFlow, XGBoost or Spark; and\n Deep learning, including transformers, test-time compute, or reinforcement learning.\n \n Salary: $212,000 - $318,000/yr.  \n This salary range represents the base salary range for the role and any sales commissions / sales bonuses targets, if applicable, would be in addition to the base salary.\n 40 hrs/week\n 50% Telecommuting Permitted.\n Multiple Positions Available. \n Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends. CA29 \n #LI-DNI","salary_min":212000,"salary_max":318000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","tensorflow","pytorch","deep-learning","data-pipeline","payments","machine-learning"],"apply_url":"https://stripe.com/jobs/search?gh_jid=8137997","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-17T22:06:29Z","expires_at":"2026-09-29T13:34:35.944205Z","created_at":"2026-08-25T18:27:17.935027Z","updated_at":"2026-08-30T13:34:36.087082Z","company_name":"Stripe","company_slug":"stripe","company_logo_url":"https://www.google.com/s2/favicons?domain=stripe.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b04c0a03-fc25-4cc1-85e1-9c6efc357643"},{"id":"d8eb6e88-6cb4-4d52-96e3-30a33b5d446e","company_id":"c93e0284-9c76-4a85-9905-494865ab9278","title":"Senior Principal Machine Learning Engineer","slug":"senior-principal-machine-learning-engineer-1289db26","description":"The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale. \n SambaNova Suite™ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations. Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets. \n About the team\n The ML team builds and optimizes the models that run on SambaNova's RDU accelerators. Their work covers model architecture, training and fine-tuning, inference optimization, evaluation, and data curation, and it lands in SambaStack and SambaCloud. They work directly with the compiler, systems, and hardware teams on co-design, so decisions about a model shape decisions about the silicon it runs on.\n About the role \n As a Senior Principal Machine Learning Engineer, you will be responsible for designing, developing, and optimizing machine learning models—with a focus on cutting-edge Large Language Models (LLMs)—to run efficiently on SambaNova's specialized hardware architecture, including the RDU. This critical role bridges advanced LLM research and practical deployment, involving the development of model architectures, improving training and inference efficiency, and collaborating on hardware-software co-design with compiler, systems, and hardware teams. The engineer will also act as the ML expert, guiding the integration of LLM solutions into production systems and customer-facing products like SambaStack and SambaCloud, with work spanning the full lifecycle from training and inference to evaluation and data curation.\n Responsibilities \n Some of your responsibilities will include:\n \n Define and drive technical strategy for ML model development, training pipelines, and inference systems on SambaNova's RDU and broader hardware ecosystem\n Lead hardware-software co-design efforts in close collaboration with compiler, systems, and hardware teams—shaping architectural decisions that unlock performance at scale\n Identify, evaluate, and champion state-of-the-art ML techniques (e.g., speculative decoding, reinforcement learning, mixture-of-experts, long-context modeling) for adoption and adaptation on reconfigurable dataflow architectures\n Serve as the senior technical voice in critical design reviews, architectural decisions, and cross-functional planning—providing guidance that influences product and engineering roadmaps\n Mentor and develop principal and senior ML engineers, elevating the technical capabilities of the organization through active collaboration, design feedback, and knowledge transfer\n Partner with product and engineering leadership to translate complex ML capabilities into scalable, customer-facing solutions in SambaStack and SambaCloud\n Drive resolution of the most complex, ambiguous technical challenges —including those that span organizational boundaries or require novel approaches not yet established in the field\n \n Required Qualifications \n \n B.S. in Computer Science, Electrical Engineering, or related field\n 8+ years of industry experience in machine learning engineering, with a demonstrated record of technical leadership on large-scale or novel ML systems\n Deep expertise in LLM training, fine-tuning, inference optimization, and evaluation at scale\n Strong background in ML algorithms, deep learning architectures, and modern training methodologies, with the ability to critically evaluate and advance the state of the art\n Demonstrated ability to lead and align cross-functional technical efforts, mentor senior engineers, and influence organizational direction without direct management authority\n Track record of independently scoping and delivering high-complexity, high-ambiguity technical projects\n \n Preferred Qualifications \n \n M.S. or Ph.D. in Computer Science, Electrical Engineering, or related field\n Experience with hardware-software co-design with non-GPU accelerators\n Publications or open-source contributions in LLM training or inference\n Experience with speculative decoding, mixture-of-experts, or long-context modeling in production\n Experience with reinforcement learning for post-training\n Familiarity with compiler or kernel-level optimization for ML workloads\n Base Salary Range:\n Base Pay Range\n $220,000 — $300,000 USD \n Submission Guidelines Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified.  \n EEO Policy SambaN","salary_min":220000,"salary_max":300000,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["deep-learning","llm","fine-tuning","generative-ai","reinforcement-learning","machine-learning"],"apply_url":"https://sambanova.ai/sambanova-available-positions/?gh_jid=6089843004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-16T15:41:38Z","expires_at":"2026-09-29T13:34:49.513266Z","created_at":"2026-08-25T18:27:21.867478Z","updated_at":"2026-08-30T13:34:49.649206Z","company_name":"SambaNova Systems","company_slug":"sambanova","company_logo_url":"https://www.google.com/s2/favicons?domain=sambanova.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d8eb6e88-6cb4-4d52-96e3-30a33b5d446e"},{"id":"c329b188-7612-4641-ac6d-796f7502eb3d","company_id":"c587b06c-b6f0-4d1d-b694-6fb6abc2a6bb","title":"Senior Research Engineer, LLM Training \u0026 Post-Training","slug":"senior-research-engineer-llm-training-post-training-b05c8bf9","description":"Who We Are \n Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.\n Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.\n We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.\n The Way We Work\n The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:\n \n Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.\n Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.\n Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.\n Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.\n Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.\n Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.\n \n  \n What We're Looking For\n We're are looking for an experienced Senior Research Engineer who has built, trained, and optimized modern transformer-based language models to join our Research Engineering function at Lightning.\n This role will focus on advancing how large language models are trained, fine-tuned, evaluated, and deployed across Lightning AI's platform and real-world customer workloads. It will work across model training, post-training, PyTorch, distributed systems, and AI systems engineering to improve model quality, training efficiency, and developer productivity while collaborating closely with researchers, infrastructure engineers, and customers.\n We're looking for someone who enjoys turning cutting-edge research into production systems. You have deep experience training and improving transformer-based language models, strong software engineering fundamentals, and a passion for solving difficult problems across model training, evaluation, and AI systems. Rather than building applications on top of existing models, you're motivated by improving the models themselves and the systems that power them. Our work spans models that power the Lightning AI platform, customer-specific model workloads, and research that translates into reusable training and platform capabilities.\n This role is hybrid with a minimum of 2 in-office days per week in San Francisco, Seattle, NYC, or London, with fully remote work considered for candidates outside of our office hub locations. All employees participate in occasional team and company offsites. \n  \n What You'll Do \n \n Design, build, and optimize training and post-training pipelines for large language models.\n Improve model quality through supervised fine-tuning, continued pretraining, preference optimization, reinforcement learning, evaluation, and experimentation.\n Build and improve PyTorch-based training infrastructure, tooling, and developer workflows.\n Optimize distributed training across multi-GPU environments by improving throughput, memory efficiency, scalability, and GPU utilization.\n Investigate model training issues, including convergence, instability, communication overhead, and performance bottlenecks.\n Design evaluation methodologies, benchmark models, analyze failure modes, and acheive model improvements through experimentation.\n Collaborate directly with customers to understand real-world workloads and translate those learnings into improvements across Lightning AI's research platform.\n Partner closely with research, infrastructure, and platform engineering teams to build production-ready AI systems.\n Contribute to open-source projects through new features, tooling improvements, documentation, and community engagement\n \n  \n What You’ll Need \n Required Qualifications \n \n Significant experience training, fine-tuning, evaluating, and/or optimizing transformer-based language models using PyTorch.\n Experience with modern LLM training and post-training techniques such as continued pretraining, SFT, RLHF, preference optimization (DPO, PPO, GRPO), reward modeling, or similar approaches.\n Strong understanding of distributed training and multi-node systems, ","salary_min":165000,"salary_max":310000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pre-training","search","fine-tuning","gpu","distributed-systems","llm","reinforcement-learning","pytorch"],"apply_url":"https://job-boards.greenhouse.io/lightningai/jobs/7860628003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T19:54:51Z","expires_at":"2026-09-29T13:33:58.434095Z","created_at":"2026-08-25T18:27:03.622997Z","updated_at":"2026-08-30T13:33:58.572247Z","company_name":"Lightning AI","company_slug":"lightning-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=lightning.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c329b188-7612-4641-ac6d-796f7502eb3d"},{"id":"9babbcca-8223-45ad-a4d0-dcb0b375e894","company_id":"af3a34e9-b9f3-4d69-bcf2-f13327711b7d","title":"Senior Reinforcement Learning Engineer","slug":"senior-reinforcement-learning-engineer-2107f97d","description":"Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better.\n JOB SUMMARY \n The Senior Reinforcement Learning  Engineer is a key, hands-on role focused on achieving state-of-the-art performance on our humanoid robots. This engineer will leverage their deep expertise in RL to solve critical locomotion and manipulation challenges and deliver breakthrough results on physical hardware. The primary focus of this role is to rapidly implement, iterate, and deploy advanced learning algorithms to push the boundaries of what our robots can do. As a senior member of the team, this individual will also be responsible for mentoring junior engineers, elevating the team's overall technical capabilities through their guidance and expertise.\n ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES \n \n Implement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.\n Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.\n Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.\n Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.\n Collaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.\n Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.\n Develop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.\n \n SKILLS AND REQUIREMENTS \n \n Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)\n Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.\n Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.\n A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.\n A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.\n A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.\n \n EDUCATION and/or EXPERIENCE \n \n A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.\n A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.\n Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.\n A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.\n \n PHYSICAL REQUIREMENTS  \n \n Prolonged periods of sitting at a desk and working on a computer\n Must be able to lift 15 pounds at times\n Vision to read printed materials and a computer screen\n Hearing and speech to communicate\n \n Compensation: The annual compensation for this position is $230,000 - $260,000 (USD) \n  \n  \n *This is a direct hire.  Please, no outside Agency solicitations. \n Apptronik provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.","salary_min":230000,"salary_max":260000,"location":"Sunnyvale, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["robotics","reinforcement-learning","fine-tuning","healthcare","distributed-systems","pytorch"],"apply_url":"https://boards.greenhouse.io/apptronik/jobs/6142286004?gh_jid=6142286004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T18:19:01Z","expires_at":"2026-09-29T13:42:55.017234Z","created_at":"2026-08-25T18:30:59.852866Z","updated_at":"2026-08-30T13:42:55.158118Z","company_name":"Apptronik","company_slug":"apptronik","company_logo_url":"https://www.google.com/s2/favicons?domain=apptronik.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9babbcca-8223-45ad-a4d0-dcb0b375e894"},{"id":"9a0613b8-75ad-4a3e-8771-40556d64a3c4","company_id":"af3a34e9-b9f3-4d69-bcf2-f13327711b7d","title":"Lead Software Engineer - Dexterous Manipulation","slug":"lead-software-engineer-dexterous-manipulation-841dfc7d","description":"Apptronik is a human-centered robotics company developing AI-powered robots to support humanity in every facet of life. Our flagship humanoid robot, Apollo, is built to collaborate thoughtfully with people, starting with critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond. We operate at the cutting edge of Applied AI, applying our expertise across the full robotics stack to solve some of society's most important problems. You will join a team dedicated to bringing Apollo to market at scale, tackling the complex challenges like safety, commercialization, and mass production to change the world for the better.\n JOB SUMMARY \n The Lead Software Engineer - Dexterous Manipulation is a core contributor to our robot’s ability to interact with the world with human-like precision. This role is responsible for leading the development of learning-based dexterous control algorithms that unlock the full potential of state-of-the-art robotic hand hardware.\n This role will bridge the gap between cutting-edge research and scalable, reliable production software. Whether leveraging reinforcement learning, imitation learning, teleoperation retargeting, or classical control, they will ensure our robots can perform complex, high-DOF tasks in both simulation and reality. As a technical lead, they will not only design the core software architecture but also influence hardware design to achieve world-class manipulation capabilities.\n ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES \n \n Strategic Ownership: Serve as the technical authority for dexterous manipulation. Create the long-term technical roadmap, ensuring hand control and multi-fingered coordination capabilities outpace industry standards.\n Architectural Definition: Design and enforce the foundational software frameworks for manipulation. Own the decision-making process for balancing autonomous logic with high-fidelity teleoperation, ensuring the architecture is scalable for future hardware generations.\n Research \u0026 Innovation: Perform and direct the integration of state-of-the-art research. Select and deploy the specific learning-based policies and vision-integrated systems that will define system physical capabilities.\n Sim-to-Real Ownership: Lead the strategy for high-fidelity simulation. Set the standards for success in virtual environments to optimize policy transitions to physical fleet hardware.\n Hardware Design Influence: Drive the specifications for next-generation hardware. Define the requirements for sensing, degrees of freedom, and torque profiles.\n Production Excellence: Oversee the transition from experimental research to fleet-wide deployment. Ensure performance and reliability of C++/Python code running on production-level assets.\n Technical Leadership \u0026 Culture: Act as a force multiplier across the organization. Beyond code reviews, foster a culture of technical rigor, setting the bar for architectural excellence and mentoring the next generation of robotics leaders.\n \n SKILLS AND REQUIREMENTS \n Technical Skills (Must-Have) \n \n Dexterous Manipulation: Deep expertise in multi-fingered hand control, grasp planning, and in-hand manipulation, with a strong track record of successful hardware deployment.\n Advanced Control \u0026 Learning: Proficiency in learning-based control for robotics (e.g. flow/diffusion-based visiomotor policies, reinforcement learning, reward modeling, etc.).\n Software Engineering: Proficiency in Python , with experience building real-time robotic software stacks.\n Simulation Environments: Experience with physics engines such as IsaacSim, MuJoCo, or Drake for policy training and validation.\n \n Good to Have \n \n Robotic Kinematics: Strong foundation in spatial transformations, Jacobian-based control, and constrained optimization.\n Teleoperation: Experience with VR/haptic interfaces and retargeting algorithms for human-in-the-loop control.\n Tactile Sensing: Experience integrating tactile/haptic feedback into manipulation pipelines.\n Computer Vision: Familiarity with 6D pose estimation, point cloud processing, or visual-servoing.\n Hardware Bring-up: Experience with the initial calibration and tuning of high-DOF robotic end-effectors.\n \n EDUCATION and/or EXPERIENCE \n \n BS/MS/PhD in Robotics, Computer Science, Electrical Engineering, or a related field.\n 5+ years of relevant experience (or 3+ years with a PhD) specifically focused on robotic manipulation or complex motion control.\n A proven track record of taking complex algorithms from a research/simulation environment and successfully deploying them on physical hardware.\n \n PHYSICAL REQUIREMENTS \n \n Prolonged periods of sitting at a desk and working on a computer   \n Must be able to lift 15 pounds at times \n Vision to read printed materials and a computer screen\n Hearing and speech to communicate   \n \n Compensation: The annual compensation for this position is $300,000 - $350,000 (USD) \n  \n  \n *This is a direct h","salary_min":300000,"salary_max":350000,"location":"Sunnyvale, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["computer-vision","healthcare","robotics","reinforcement-learning"],"apply_url":"https://boards.greenhouse.io/apptronik/jobs/6142284004?gh_jid=6142284004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T18:17:26Z","expires_at":"2026-09-29T13:42:53.426667Z","created_at":"2026-08-25T18:30:59.776963Z","updated_at":"2026-08-30T13:42:53.564182Z","company_name":"Apptronik","company_slug":"apptronik","company_logo_url":"https://www.google.com/s2/favicons?domain=apptronik.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9a0613b8-75ad-4a3e-8771-40556d64a3c4"},{"id":"5003cb2a-ee5e-49a1-8607-cf2e7db035db","company_id":"4bc4e268-7a05-4a65-a162-1688af546f7e","title":"Machine Learning Programmer, Memory","slug":"machine-learning-programmer-memory-78a2e989","description":"WHAT MAKES US EPIC?\n At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.\n Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.\n PROGRAMMING - GAMES\n What We Do \n Unreal projects have been leading the pack of real-time entertainment with our constantly growing team of programming experts. We’re always improving on the tools and technology that empower content developers worldwide.\n What You'll Do \n As foundation models move from answering questions to acting over long horizons, memory becomes a central requirement. Systems must decide what is worth keeping and improve from experience. Large context windows and retrieval systems are not enough. You will work with researchers on projects across the company that require memory in different modalities to define general solutions that enable models to represent experience, decide what they keep and what they discard, and to efficiently store, retrieve and exploit memory.\n In this role, you will \n \n Design new memory architectures that work across problems, modalities and products\n Define new ways to measure and evaluate memory performance \n Help create and deliver production-ready, scalable and high-quality learning models and associated algorithms \n Critically assess the effectiveness of such models and make recommendations for the ongoing roadmap \n \n What we're looking for \n \n PhD in Computer Science, Mathematics or related field, or 3+ years of relevant industry experience \n Experience creating machine learning algorithms for vision and/or language problems and deploying them as production-level systems \n Expert, hands-on knowledge of: \n \n Foundational models and vision and/or language, incl. local deployment and model post-training of open weights models \n Memory, state, or long-horizon behaviour in foundation models: your experience may come from language agents and long-context methods, reinforcement learning under partial observability, world models, or sequence modelling \n Python and associated ML tools/frameworks (numpy, scipy, sklearn, pytorch) \n \n Experience with working collaboratively with other developers, following a flexible and agile approach \n \n This role is open to multiple locations across North America \u0026 Europe (including CA). \n Pay Transparency Information \n The expected annual base pay range(s) for this position are detailed below. Each base pay range is relevant only for individuals who are residents of or will be expected to work within the specified locale. Compensation varies based on a variety of factors, which include (but aren’t limited to) things such as skills and competencies, qualifications, knowledge, and experience. In addition to base pay, most employees are eligible to participate in Epic’s generous benefit plans and discretionary incentive programs (subject to the terms of those plans or programs). \n California Base Pay Range\n $184,481 — $270,571 USD \n ABOUT US\n Epic Games​ ​is a leading interactive entertainment company. For over 30 years we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent.\n Like what you hear? Come be a part of something Epic! \n Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer. Learn more about our Equal Employment Opportunity (EEO) Policy here .\n Note to Recruitment Agencies: Epic does not accept any unsolicited resumes or approaches from any unauthorized third party (including recruitment or placement agencies) (i.e., a third party with whom we do not have a negotiated and validly executed agreement). We will not pay any fees to any unauthorized third party. 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In this role, you’ll work across operations, engineering, customer engagement, and directly with our clients to produce world-class cyber test and evaluation and training data for Large Language Models for our Public Sector customers. \n This role offers a rare opportunity to make a meaningful impact at the intersection of cyber, AI and national security. You will build human data labeling pipelines from the ground up, create operational processes to manage and optimize an in-house expert data workforce, and develop novel technology-driven approaches (e.g., scripts, prompt engineering, hybrid data) to improve the quality of both our training and evaluation datasets. You will also own the financial and technical viability of your programs by managing project COGS and partnering with Go-to-Market teams to scope customer engagements through taxonomy design and feasibility validation, ensuring deals are scalable, executable, and economically sound. In addition, you will partner directly with our internal machine learning experts and external stakeholders to ensure our data enables the development of mission-critical applications of AI. \n Help shape the future of AI by joining a fast-growing team built on exceptional data, tools, and systems.\n You Will:  \n \n Develop, build, and maintain the operations infrastructure required to ensure data labeling pipelines are efficient, scalable, and produce high-quality outputs.\n Partner closely with customers to understand their requirements and design dataset taxonomies that evaluate agents and models or improve model performance.\n Take ownership of day-to-day progress on high-priority data production pipelines, ensuring projects move forward efficiently\n Partner with subject matter experts in their fields to validate the quality of our data and to translate deep cyber domain knowledge into scalable processes and measurable outcomes.   \n Work with our Machine Learning and Go-to-Market teams to scope customer engagements via technical feasibility validation to ensure deals are scalable and executable.\n Influence cross-org collaboration to define and advance human data strategy, influencing technical and non-technical stakeholders to ensure data quality, scalability, and long-term platform leverage.   \n Own the financial health of programs by managing project-level COGS through workforce planning, tooling decisions, and process optimization.\n Utilize analytics and data visualization tools to track progress, identify bottlenecks, and make data-driven decisions to optimize pipeline performance\n Own larger and larger components of our data delivery processes, until you ultimately serve as the full owner of our most visible and high impact customer pipelines\n \n  \n You have:  \n \n An active Top Secret security clearance \n Demonstrated experience in cybersecurity domains such as penetration testing, red/blue teaming, vulnerability assessment, network security, or cyber operations.\n 2-3 years of experience in product development, data science, or operations\n A history of successful project management and comfort in ambiguity\n Ability to analyze complex operational data, build queries, and identify trends to inform decisions and optimize processes\n Technical aptitude to understand how to produce data for state of the art post-training techniques such as supervised fine tuning (SFT), reinforcement learning through human feedback (RLHF), Reinforcement Learning with Verifiable Rewards (RLVR) etc \n \n  \n Nice to have:  \n \n Experience working in defense tech and/or an AI company\n A technical degree in fields like computer science, data science, or engineering\n A deep understanding of ML operations for generative AI workflows / products\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. 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Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.\n In this hybrid role, you will report to a Senior Research Scientist.\n You will: \n \n Develop and extend cutting-edge research in robotics and machine learning to advance state-of-the-art recipes for advancing the quality, safety, and realism of embodied AI agents\n Partner within and across organizations to land disruptive and innovative tech in production\n Work with a variety of state-of-the-art Foundation Models \n Drive model development via data, eval, and systems\n Implement and extend large large scale data and evaluation pipeline\n \n You have: \n \n Masters degree in Computer Science, Machine Learning, Robotics, similar technical field of study, or equivalent practical experience\n Proficiency in Python\n Familiarity with one of the modern deep learning frameworks (e.g. Pytorch, JAX, Tensorflow)\n Prior work in an industrial or research setting developing recipes for ML models\n \n We prefer: \n \n Track record of publications in top-tier conferences or leading open source projects in the related fields\n Strong hands-on SWE skills, able to design, implement, and extend large distributed pipelines\n Experience in AV planning and related research\n Experience in labeling and curating data for ML eval and training\n \n  \n In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:\n Health, dental, vision, life, disability insurance Retirement Benefits: 401(k) with company match Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary) Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks Baby Bonding Leave: 18 weeks Holidays: 13 paid days per year \n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $175,000 — $215,000 USD","salary_min":175000,"salary_max":215000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["deep-learning","tensorflow","reinforcement-learning","autonomous-vehicles","agents","generative-ai","pytorch","robotics"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8109035","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-10T14:40:16Z","expires_at":"2026-09-29T13:35:05.036552Z","created_at":"2026-08-25T18:27:25.761878Z","updated_at":"2026-08-30T13:35:05.172268Z","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/4b6dd3fa-7bf0-45bb-9581-df7e0937c37f"}],"page":1,"per_page":20,"total":556,"total_is_exact":true,"total_pages":28}