Reinforcement Learning Engineer

About the Company

Humanoid is on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. Their HMND-01 Alpha platform is already running in real industrial pilots.

Responsibilities

• Design and train reinforcement learning policies for humanoid robot control
• Build scalable simulation and training pipelines using tools like Isaac Lab or 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 robot control stack

Requirements

• MS or PhD in Robotics, Machine Learning, Computer Science, or a 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++

Preferred Qualifications

• Experience with RL for locomotion or legged robots
• Experience with sim-to-real transfer
• Familiarity with robot dynamics, control, or whole-body control

Benefits

• Comprehensive health coverage (medical, dental, vision)
• 23 days of PTO, separate sick leave, and paid holidays
• 401(k) retirement plan with employer match
• Equity packages
• Free daily catered lunch, snacks, and drinks in-office