{"id":2301348,"url":"https://alion.io/job/mobileye-reinforcement-learning-engineer-whole-body-control","title":"Reinforcement Learning Engineer - Whole-Body Control","company":{"id":174,"name":"Mobileye","domain":"mobileye.com","url":"https://alion.io/company/mobileye","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":91,"open_postings":58,"ghost_share":0,"stale_share":0.379,"repost_share":0,"time_to_fill_p50_days":56,"computed_at":"2026-10-24T06:02:35Z"}},"role":"Robotics","role_family":"Robotics","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Shanghai, China"],"countries":["CN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":44000,"max_usd":105000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":255},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Isaac Lab","optional":false},{"name":"Isaac Sim","optional":false},{"name":"PPO","optional":false},{"name":"PyTorch","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Teleoperation","optional":false}],"status":"live","first_seen_at":"2026-06-25T08:23:19Z","employer_posted_date":"2026-06-25","last_verified_at":"2026-10-24T23:48:26Z","board_verified":true,"closed_at":null,"days_open":121,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":120},"description":"At Mentee Robotics, we are redefining humanoid automation with an AI-first approach - combining perception, reasoning, and dexterous manipulation into fully autonomous systems that continuously learn and adapt.\nWe are now expanding with a new robotics Engineering Center in China, working hand-in-hand with our engineering teams in headquarters. Its mission: to rapidly develop our next-generation full-size humanoid and bring it to life - a walking, working platform that becomes the foundation of our next generation of products. This is a small, senior, hands-on team where speed of iteration is the core value.\nWe are looking for an RL Engineer to train the whole-body behaviors of our humanoid in simulation, on Isaac Sim with the Newton physics engine. In our architecture there is no classical motion controller above the joint level - the learned policy is the robot's entire behavior layer, coordinating all degrees of freedom and commanding joints directly through the actuator controllers. You are on the critical path to the robot's first steps.\nWho you are 期待中的你\nA deep RL practitioner who has actually transferred policies to physical legged robots - not only benchmarks\nStrong engineer first: your training code is infrastructure, not a notebook\nComfortable being the owner of the robot's most visible capability\nResponsibilities 岗位职责\nDesign, train, and iterate whole-body RL policies in Isaac Sim/Newton: walking, balance recovery, manipulation, and coordinated loco-manipulation behaviors\nOwn reward design, curriculum learning, and training methodology; incorporate motion priors (mocap/imitation, AMP-style) for natural movement\nBuild and maintain the training infrastructure: massively parallel simulation, experiment tracking, evaluation suites\nWork with the Sim2Real engineer to bake measured actuator constraints and domain randomization into training\nWork with the compute platform engineer to deploy policies on the robot and run on-hardware evaluation\nProgressively expand the policy's capability envelope - from first steps to dynamic, contact-rich whole-body tasks\nDefine what the policy observes and commands together with motion control and platform teams - you co-own the robot's core software contract\nRequirements任职要求\nM.Sc. or Ph.D. (or equivalent industry experience) in Computer Science, Robotics, or a related field\n5+ years of hands-on deep RL for robotics with strong PyTorch engineering skills\nDirect experience with Isaac Sim/Newton (or equivalent GPU-parallel simulators) for whole-body RL on legged robots\nProven sim-to-real transfer of at least one policy to a physical legged robot\nDeep understanding of PPO-family training at scale, reward shaping, and curriculum design\nAdvantages加分项\nFamiliarity with IsaacLab\nHumanoid (vs. quadruped) whole-body RL experience\nExperience with motion-imitation methods (AMP, DeepMimic-style) and mocap data pipelines\nPublications in top robotics/ML venues (RSS, CoRL, ICRA, NeurIPS) or experience at leading humanoid teams\nExperience with teleoperation or demonstration data pipelines for whole-body skills\nComfortable communicating technical topics in English with international teams","description_format":"text","description_chars":3163,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":true}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","Autonomous Driving","Computer Vision","Automotive Components"],"lifecycle":[{"event":"open","at":"2026-10-11T21:06:23Z"}],"visa":[],"liveness":{"score":18,"band":"cold","label":"Long shot","p_open":1,"p_active":0.647,"p_room":0.28,"age_days":120,"expected_fill_days":56,"reasons":["conf:1","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-24T06:02:35Z"},"pay":null,"html_url":"https://alion.io/job/mobileye-reinforcement-learning-engineer-whole-body-control","json_url":"https://alion.io/job/mobileye-reinforcement-learning-engineer-whole-body-control.json","meta":{"generated_at":"2026-10-24T23:49:29Z","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":34959,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-25T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"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":174},"rest":"https://alion.io/mcp/rest/get_company?id=174"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fmobileye-reinforcement-learning-engineer-whole-body-control"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"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%2Fmobileye-reinforcement-learning-engineer-whole-body-control"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"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%2Fmobileye-reinforcement-learning-engineer-whole-body-control"},{"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/mobileye-reinforcement-learning-engineer-whole-body-control\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fmobileye-reinforcement-learning-engineer-whole-body-control"}]}