Humanoid
Reinforcement Learning Engineer - Locomanipulation
US, Boston, MA
Sponsorship not specified$200k-$350kDetected 96 days ago
PythonRESTMachine LearningRoboticsCollaboration
About the role
- You will work closely with control and robotics engineers to integrate learned policies into the robot control stack, ensuring stable and robust behavior in real-world conditions.
- Development will involve continuous iteration between large-scale simulation and hardware experiments.
- The problems you will work on include dynamic locomotion, balance recovery, contact-rich manipulation, and multi-behavior policy learning.
Responsibilities
- Build scalable simulation and training pipelines (e.g., Isaac Lab, MuJoCo).
- Design reward functions, observation spaces, and curricula for complex behaviors.
- That's why we've set out on a mission to build the world's most capable, commercially-scalable, and safe humanoid robots.
- Freedom to influence the product and own key initiatives.
Requirements
- Experience applying RL to robotics or physical systems.
- Experience deploying learned policies on real robotic systems.
- Experience with physics-based simulation environments (e.g., Isaac Lab, MuJoCo).
Nice to have
- Experience with RL for locomotion or legged robots.
- Experience with sim-to-real transfer.
- Familiarity with robot dynamics, control, or whole-body control.
Compensation
- For this role in Massachusetts, the expected base salary range is $200K-$350K USD per year
- For this role in Massachusetts, the expected base salary range is $200K-$350K USD per year; your placement in that range depends on how your experience maps to our internal leveling.
Benefits
- Comprehensive health coverage for US‑based employees, including fully paid medical, dental, and vision insurance, with virtual care and employee assistance resources.
- Meaningful time off to rest and recharge: 23 days of PTO (accrued), separate sick leave, and paid company holidays.
- 401(k) retirement plan with employer match.
- Equity included-we believe builders should share in what they build.
- Design and train reinforcement learning policies for humanoid robot control.
- MS or PhD in Robotics, Machine Learning, Computer Science, or related field.
- Strong experience with reinforcement learning (e.g., PPO, SAC, offline RL).
Company info
- Here at Humanoid, we believe in a future where robots amplify human potential.
Apply directly at Humanoid →Create a free account for alerts like thisView Humanoid immigration profile
This listing is sourced directly from Humanoid's careers page and normalized into a canonical job model.