{"id":127560,"url":"https://alion.io/job/boston-dynamics-staff-reinforcement-learning-research-engineer","title":"Staff Reinforcement Learning Research Engineer","company":{"id":47994,"name":"Boston Dynamics","domain":"bostondynamics.com","url":"https://alion.io/company/boston-dynamics","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":83,"open_postings":3,"ghost_share":0,"stale_share":0.667,"repost_share":0,"time_to_fill_p50_days":50,"computed_at":"2026-10-22T06:10:10Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Waltham, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":155284,"max":200000,"currency":"USD","period":"year","gross":null,"usd_annual":200000},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Bazel","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Isaac Lab","optional":false},{"name":"JAX","optional":false},{"name":"Kubernetes","optional":false},{"name":"MuJoCo","optional":false},{"name":"Multimodal AI","optional":false},{"name":"ONNX","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLlib","optional":false},{"name":"Sim-to-Real","optional":false},{"name":"Stable-Baselines3","optional":false},{"name":"TensorRT","optional":false},{"name":"Triton","optional":false},{"name":"Isaac Sim","optional":true}],"status":"live","first_seen_at":"2026-08-06T00:00:00Z","employer_posted_date":"2026-08-06","last_verified_at":"2026-10-21T15:07:34Z","board_verified":true,"closed_at":null,"days_open":77,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":77},"description":"Do you want to build the scalable reinforcement learning framework that powers the next generation of humanoid and quadruped robots? As a Staff RL Research Engineer, you'll own the RL stack, including massively parallel simulation, domain randomization, policy optimization, and on-robot deployment. Your job is to make the pipeline fast, reliable, and reproducible. You'll work alongside world-class engineers and scientists pushing the boundaries of whole-body control and dexterous manipulation.\nIn this role, you will:\nImplement on-policy and off-policy learning algorithms\n\nScale GPU-accelerated simulation to generate millions of samples per second\n\nCrack sim-to-real to produce policies that transfer to the physical robot\n\nIntegrate RL with VLAs to fine-tune and distill large multimodal policies\n\nMake deployment easy, fast, and reproducible\n\nBuild visualization tools that enable data-driven research\n\nRequired Qualifications\nMS with 3+ years of experience, or PhD, in ML, Robotics, or a related field\n\nDeployed policies on physical robots with attention to latency, robustness, and safety\n\nExpertise with RL toolboxes (RSL-RL, CleanRL, RLlib, Stable Baselines)\n\nExpertise with simulation and rendering tooling (Isaac Lab, MuJoCo, MjWarp, MjLab)\n\nProficient in PyTorch and/or JAX, plus inference runtimes (ONNX, Triton, TensorRT)\n\nSolid software fundamentals: Bazel, monorepos, Docker, CI/CD\n\nThe ideal candidate has:\nBuilt production-grade RL training pipelines\n\nDeep knowledge of GPU-accelerated physics simulation\n\nApplied RL to humanoid locomotion, whole-body control, or dexterous manipulation\n\nWorked on sim-to-real transfer, domain randomization, or system identification\n\nExperience with heterogeneous compute clusters and Kubernetes\n\nWhy join us?\nOwnership of the company wide RL tools powering all of our robots\n\nDirect access to the compute infrastructure to run large-scale experiments\n\nThe chance to help define what’s possible in real-world robotics\n\nThe salary or hourly pay range for this position will be clearly stated in the job posting as required by Massachusetts law. The base pay range for this position is between $155,284.34- $200,000. Base pay will depend on multiple individualized factors including, but not limited to internal equity, job related knowledge, skills and experience. This range represents a good faith estimate of compensation at the time of posting. Boston Dynamics offers a generous Benefits package including medical, dental vision, 401(k), paid time off and an annual bonus structure. 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