Reinforcement Learning Engineer - Locomanipulation
About the role
We are looking for a Senior or Staff Reinforcement Learning Engineer to develop learning-based control policies for humanoid robots. You 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.
Responsibilities
- Design and train reinforcement learning policies for humanoid robot control.
- Build scalable simulation and training pipelines (e.g., Isaac Lab, MuJoCo).
- Design reward functions, observation spaces, and curricula for complex behaviors.
- Improve robustness and sim-to-real transfer of learned policies.
- Deploy and evaluate policies on real robotic systems.
- Integrate policies into the control stack.
Requirements
- MS or PhD in Robotics, Machine Learning, Computer Science, or related field.
- Strong experience with reinforcement learning (e.g., PPO, SAC, offline RL).
- 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).
- Strong programming skills in Python and/or C++.
Qualifications
- 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.
Skills
- Experience with reinforcement learning algorithms.
- Experience with robotics and physical systems.
- Experience with simulation environments.
- Programming skills in Python and/or C++.
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.
Free daily catered lunch, snacks, and drinks in-office.
Collaboration with top-tier engineers, researchers, and product experts in AI and robotics.
Freedom to influence the product and own key initiatives.
Pay
The expected base salary range for this role in Massachusetts is $200K–$350K USD per year; your placement in that range depends on how your experience maps to our internal leveling.
Schedule
Not specified.