Reinforcement Learning Engineer - Locomanipulation

📋 Description

• Design and train reinforcement learning policies for humanoid robot control.
• Build scalable simulation and training pipelines (Isaac Lab, MuJoCo).
• Design reward functions, observation spaces, and curricula.
• Improve robustness and sim-to-real transfer of policies.
• Deploy and evaluate policies on real robotic systems.
• Integrate policies into the robot control stack.

🎯 Requirements

• MS or PhD in Robotics, ML, CS, 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 (Isaac Lab, MuJoCo).
• Strong programming skills in Python and/or C++.

🎁 Benefits

• Comprehensive health coverage for US-based employees (medical, dental, vision).
• 23 days PTO, separate sick leave, and paid holidays.
• 401(k) retirement plan with employer match.
• Equity included – share in what you build.
• Free daily catered lunch, snacks, and drinks in-office.
• Collaboration with top-tier engineers, researchers, and product experts.