Simulation And Reinforcement Learning Engineer

Senior Simulation & Reinforcement Learning Engineer

We are seeking a Senior Simulation & Reinforcement Learning Engineer to join a fast-moving R&D team developing next-generation humanoid robotics. This role combines high-fidelity simulation with advanced reinforcement learning workflows to enable rapid prototyping and deployment on physical hardware. You will design reproducible simulation studies, integrate RL policies, and enhance model fidelity while collaborating closely with mechanical and electrical teams.

Key Responsibilities:

• Implement whole-body humanoid analysis in Isaac Lab or similar simulators.
• Build version-controlled, tested, and documented scripts for simulation studies.
• Connect full-body RL policies to simulation environments and optimize for sim-to-real transfer.
• Extend models with realistic effects (friction, efficiency, backlash, impact loads) and validate against design specs.
• Collaborate cross-functionally to unblock design iterations and communicate technical insights clearly.

Skills and Requirements:

• Expert-level Python (NumPy, PyTorch)
• Experience with at least one modern simulator (Isaac Lab/Sim, MuJoCo, Brax, etc.)
• Hands-on RL workflow experience (training, inference, hyperparameter tuning)
• Strong proficiency in C++ for performance-critical code
• Proven track record of shipping research prototypes and production-grade code under Git
• BS in Robotics, CS, ME, EE, or related + 4 years relevant experience OR MS + 2 years relevant experience
• GPU/cloud experience and containerization (Docker)
• Prior work in contact/dynamics modeling or system identification
• Familiarity with URDF and USD model formats; experience with Mojoco, Gazebo, or similar tools
• Publications in top robotics/ML conferences (CoRL, RSS, ICRA)
• Strong GitHub portfolio showcasing simulation and RL projects