{"id":91888,"url":"https://alion.io/job/humanoid-reinforcement-learning-engineer-locomanipulation","title":"Reinforcement Learning Engineer - Locomanipulation","company":{"id":35230,"name":"Humanoid","domain":"thehumanoid.ai","url":"https://alion.io/company/thehumanoid","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":78,"open_postings":10,"ghost_share":0,"stale_share":0.9,"repost_share":0,"time_to_fill_p50_days":58,"computed_at":"2026-10-17T05:46:00Z"}},"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":["London, United Kingdom"],"countries":["GB"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":91000,"max_usd":219000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":243},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"C++","optional":false},{"name":"Isaac Lab","optional":false},{"name":"Machine Learning","optional":false},{"name":"MuJoCo","optional":false},{"name":"PPO","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Isaac Sim","optional":true}],"status":"live","first_seen_at":"2026-08-13T15:18:51Z","employer_posted_date":"2026-08-13","last_verified_at":"2026-10-17T05:43:14Z","board_verified":true,"closed_at":null,"days_open":64,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":64},"description":"Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND-01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further.\nAbout The Role\nWe are looking for a Senior or Staff Reinforcement Learning Engineer to develop learning-based control policies for humanoid robots.\nYou will design and train reinforcement learning policies that enable dynamic locomotion and loco-manipulation behaviors on real robots. Your work will focus on building scalable training pipelines, designing reward functions and environments, and improving sim-to-real transfer for reliable deployment on hardware.\nYou will work closely with controls and robotics engineers to integrate learned policies into the robot control stack, ensuring stable and robust behavior in real-world conditions.\nDevelopment will involve continuous iteration between large-scale simulation and hardware experiments.\nThe problems you will work on include dynamic locomotion, balance recovery, contact-rich manipulation, and multi-behavior policy learning.\nWhat You’ll Do\nDesign and train reinforcement learning policies for humanoid robot control.\n\nBuild scalable simulation and training pipelines (e.g., Isaac Lab, MuJoCo).\n\nDesign reward functions, observation spaces, and curricula for complex behaviors.\n\nImprove robustness and sim-to-real transfer of learned policies.\n\nDeploy and evaluate policies on real robotic systems.\n\nIntegrate policies into the control stack.\n\nWhat We're Looking For\nMS or PhD in Robotics, Machine Learning, Computer Science, or related field.\n\nStrong experience with reinforcement learning (e.g., PPO, SAC, offline RL).\n\nExperience applying RL to robotics or physical systems.\n\nExperience deploying learned policies on real robotic systems.\n\nExperience with physics-based simulation environments (e.g., Isaac Lab, MuJoCo).\n\nStrong programming skills in Python and/or C++.\n\nNice to have:\nExperience with RL for locomotion or legged robots.\n\nExperience with sim-to-real transfer.\n\nFamiliarity with robot dynamics, control, or whole-body control.\n\nWhat We Offer\nCompetitive equity: stock options with meaningful upside as we scale.\n\n30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas-New Year shutdown).\n\nPrivate healthcare, including virtual and in-person care.\n\nPension scheme with 8% total contribution (5% employee, 3% employer) on full earnings.\n\nFree daily breakfast, catered lunch, and snacks in-office.\n\nWork at the frontier - 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