{"id":127521,"url":"https://alion.io/job/apptronik-senior-reinforcement-learning-engineer-2","title":"Senior Reinforcement Learning Engineer","company":{"id":47983,"name":"Apptronik","domain":"apptronik.com","url":"https://alion.io/company/apptronik","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":83,"open_postings":23,"ghost_share":0,"stale_share":0.478,"repost_share":0,"time_to_fill_p50_days":77,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sunnyvale, United States","Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":230000,"max":260000,"currency":"USD","period":"year","gross":null,"usd_annual":260000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Imitation Learning","optional":false},{"name":"Isaac Gym","optional":false},{"name":"JAX","optional":false},{"name":"MuJoCo","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Teleoperation","optional":false}],"status":"live","first_seen_at":"2026-08-12T18:19:01Z","employer_posted_date":"2026-09-28","last_verified_at":"2026-10-18T09:23:17Z","board_verified":true,"closed_at":null,"days_open":66,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":66},"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.\nWe 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.\nJOB SUMMARY\nThe 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.\nESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES\nImplement and deploy state-of-the-art RL algorithms to achieve ambitious, world-class performance on dynamic locomotion and manipulation tasks with physical hardware.\nDrive the entire development cycle, from prototyping in simulation to robustly transferring and fine-tuning policies on the robot.\nOptimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high-throughput simulation and distributed training.\nMentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.\nCollaborate closely with the robotics and hardware teams to diagnose system-level issues and co-develop solutions that enable more complex learned behaviors.\nAnalyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.\nDevelop and refine motion retargeting pipelines to translate human demonstration data (mocap, teleoperation) into robust reference trajectories for reinforcement learning.\nSKILLS AND REQUIREMENTS\nDeep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)\nMastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.\nExperience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization.\nA strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model-based RL, and sim-to-real transfer techniques.\nA strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches.\nA results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware.\nEDUCATION and/or EXPERIENCE\nA PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.\nA proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators.\nDemonstrated experience mentoring or providing technical guidance to other engineers in a team environment.\nA strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.\nPHYSICAL REQUIREMENTS \nProlonged periods of sitting at a desk and working on a computer\nMust be able to lift 15 pounds at times\nVision to read printed materials and a computer screen\nHearing and speech to communicate\nCompensation: The annual compensation for this position is $230,000 - $260,000 (USD) \n*This is a direct hire. Please, no outside Agency solicitations. \nApptronik 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.","description_format":"text","description_chars":4633,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Robotics AI","Robotic Components & Sensors","Humanoid Robots"],"lifecycle":[{"event":"open","at":"2026-08-12T18:19:01Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 23","filings_12m":23,"filings_prev_12m":20,"green_card_filings_12m":0,"median_offered_wage_usd":157500,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)"],"filings_for_role_12m":0}],"liveness":{"score":50,"band":"ok","label":"Likely open","p_open":1,"p_active":0.831,"p_room":0.6,"age_days":65,"expected_fill_days":77,"reasons":["conf:1","velocity","win:late","crowd:"],"computed_at":"2026-10-17T05:46:00Z"},"pay":{"stated_usd_annual":260000,"is_top_pay":true},"html_url":"https://alion.io/job/apptronik-senior-reinforcement-learning-engineer-2","json_url":"https://alion.io/job/apptronik-senior-reinforcement-learning-engineer-2.json","meta":{"generated_at":"2026-10-18T10:44:26Z","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":15942,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-19T00:00:00Z"}},"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":47983},"rest":"https://alion.io/mcp/rest/get_company?id=47983"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fapptronik-senior-reinforcement-learning-engineer-2"},{"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%2Fapptronik-senior-reinforcement-learning-engineer-2"},{"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%2Fapptronik-senior-reinforcement-learning-engineer-2"},{"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/apptronik-senior-reinforcement-learning-engineer-2\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fapptronik-senior-reinforcement-learning-engineer-2"}]}