{"id":4089616,"url":"https://alion.io/job/general-motors-applied-robot-learning-engineer-rl-specialist","title":"Applied Robot Learning Engineer / RL Specialist","company":{"id":1705,"name":"General Motors","domain":"gm.com","url":"https://alion.io/company/general-motors-europe","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":91,"open_postings":84,"ghost_share":0,"stale_share":0.536,"repost_share":0.036,"time_to_fill_p50_days":15,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Robotics","role_family":"Robotics","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mountain View, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":153200,"max":234100,"currency":"USD","period":"year","gross":null,"usd_annual":234100},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Fine-tuning","optional":false},{"name":"Foxglove Studio","optional":false},{"name":"Git","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Imitation Learning","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reward Modeling","optional":false},{"name":"ROS","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Teleoperation","optional":false},{"name":"Vision-Language-Action","optional":false},{"name":"VLM","optional":false},{"name":"World Models","optional":false},{"name":"OpenVLA","optional":true}],"status":"live","first_seen_at":"2026-11-11T19:51:52Z","employer_posted_date":"2026-11-11","last_verified_at":"2026-11-12T12:18:21Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Job Description\nApplied Robot Learning Engineer/RL Specialist\nFocus: Reinforcement Learning & Policy Optimization\nAbout the Role\nWe are looking for an experienced applied robotics/robot learning engineer with strong reinforcement learning expertise and hands-on experience deploying learning-based policies for complex manufacturing tasks. The role primarily focuses on improving the performance of VLA/world models, addressing sim-to-real challenges, and developing robust and scalable policies for manipulation, failure recovery, and optimal execution.\nThis is a hands-on role requiring real-world deployment experience, comfort working directly with robotic hardware, and the flexibility to support broader engineering and experimental tasks as project priorities evolve.\nThe ideal candidate combines strong RL fundamentals with practical robotics engineering experience and some exposure to bimanual manipulation.\nApproximately 80% of the role will focus on applied engineering, policy implementation, robot experiments, evaluation, and deployment, with 20% dedicated to research and technical exploration. The emphasis is on translating RL methods into measurable improvements on physical robots rather than pursuing research in isolation.\nKey Responsibilities\nDevelop, fine-tune, and evaluate VLA models and learning-based policies on physical robotic systems. \nApply reinforcement learning to improve manipulation success rates, execution efficiency, robustness, and recovery from failures. \nImplement and evaluate appropriate approaches such as PPO, SAC, residual RL, policy adaptation, or learned recovery policies. \nDesign reward functions and success detectors, including learned or VLM-based reward models, for real-world manipulation tasks. \nApply sample-efficient methods for real-world training, such as RL from demonstrations, offline-to-online RL, and human-in-the-loop corrections. \nSet up safe real-robot RL, including action limits, workspace constraints, automated resets, and supervised exploration. \nDiagnose sim-to-real gaps and improve real-world performance using techniques such as domain randomization, system identification, and real-world fine-tuning. \nApply learned policies to manipulation tasks, including tasks involving bimanual setups and coordinated robot execution. \nDesign evaluations, analyze failure modes, and measure improvements in success rate, cycle time, generalization, and repeatability. \nWork directly with robotic hardware to run experiments, debug policies, and validate improvements. \nSupervise data collection, identify data-quality issues, and coordinate corrections to demonstrations or datasets. \nCollaborate with the simulation team and contribute to benchmarking, model evaluation, and other hands-on engineering tasks as needed. \nRequired Qualifications\nPh.D. in Computer Science, Robotics or a related discipline or Master’s with 3 or more years of industry experience. \nStrong applied expertise in reinforcement learning for robotics, including policy optimization, reward design, training, and evaluation. \nExperience deploying or validating learned policies on physical robots; simulation-only experience is insufficient. \nExperience with sim-to-real transfer and diagnosing discrepancies between simulated and real-world execution. \nExperience with Git, Python, PyTorch, and ROS/ROS 2, combined with hands-on familiarity with edge compute platforms (NVIDIA Jetson) and robotics MLOps ecosystems (experiment tracking, dataset versioning, and visualization tools like Foxglove). \nExperience with sensor calibration (eye-in-hand/eye-to-hand camera calibration), force-torque sensors, or teleoperation data collection setups. \nUnderstanding of imitation learning, robot kinematics, control interfaces, and manipulation workflows. \nComfort working directly with robots and taking ownership of experiments, model training, evaluations, data quality, debugging, and deployment. \nAdaptability and willingness to contribute beyond core model development when required to meet project goals. \nPreferred Qualifications\nExperience combining RL with pretrained VLA policies or imitation learning. \nExperience with residual RL, online policy adaptation, failure recovery, or contact-rich manipulation. \nExperience with VLA models such as Pi0/Pi0.5, OpenVLA, Groot, or comparable architectures. \nExperience with industrial or collaborative robots and real-world manufacturing applications is a plus. \nHands-on experience with Fanuc/UR robots is a plus. \nCollaboration & Working Style\nOwnership and Accountability: Take ownership of assigned tasks from problem identification through implementation, testing, and deployment. Proactively identify issues, follow through action items, and communicate blockers early. \nTeamwork: Work effectively with cross-functional teams. Share knowledge, provide constructive feedback, and contribute toward shared project goals. \nPassion for Experimentation: Enjoy testing ideas rapidly, working directly with robots, debugging failures, and turning promising approaches into working solutions. Be willing to iterate repeatedly and learn from unsuccessful experiments. \nAdaptability: Be willing to take on responsibilities beyond core model development, including evaluation, data quality checks, debugging, benchmarking, and experimental setup, based on project priorities. \nOn-site 5x days per week, Mountain View, CA\nCompensation: The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of the California Bay Area.\nThe salary range for this role is $153,200 to $234,100. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.\nBonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.\nBenefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.\n\nAbout GM\nOur vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.\nWhy Join Us\nWe believe we all must make a choice every day - individually and collectively - to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.\nBenefits Overview\nFrom day one, we're looking out for your well-being-at work and at home-so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.\nNon-Discrimination and Equal Employment Opportunities (U.S.)\nGeneral Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.\nAll employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.\nWe encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.\nAccommodations\nGeneral Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.","description_format":"text","description_chars":8626,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":["Life insurance","Retirement plans"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Automotive Manufacturing"],"lifecycle":[{"event":"open","at":"2026-11-11T19:51:52Z"}],"visa":[{"country":"US","licensed_sponsor":true,"adverts_offering_sponsorship_24m":0,"sanctioned_now":false,"evidence":"H-1B filings in 12 months: 795 · green card filings: 1","filings_12m":795,"filings_prev_12m":812,"green_card_filings_12m":1,"median_offered_wage_usd":147456,"route":null,"cap_exempt":false,"checked_at":"2026-11-12T07:36:15+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":529}],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":0,"expected_fill_days":15,"reasons":["conf:4","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-11-12T10:38:37Z"},"pay":{"stated_usd_annual":234100,"is_top_pay":true},"html_url":"https://alion.io/job/general-motors-applied-robot-learning-engineer-rl-specialist","json_url":"https://alion.io/job/general-motors-applied-robot-learning-engineer-rl-specialist.json","meta":{"generated_at":"2026-11-12T23:15:00Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":49166,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-13T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":1705},"rest":"https://alion.io/mcp/rest/get_company?id=1705"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fgeneral-motors-applied-robot-learning-engineer-rl-specialist"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"free","price":{"credits":0,"usd":0},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fgeneral-motors-applied-robot-learning-engineer-rl-specialist"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fgeneral-motors-applied-robot-learning-engineer-rl-specialist"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/general-motors-applied-robot-learning-engineer-rl-specialist\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fgeneral-motors-applied-robot-learning-engineer-rl-specialist"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":4089616}},{"tool":"search_jobs","arguments":{"company":"gm.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":4089616},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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