Reinforcement Learning Engineer – Whole Body Control

About this role

Figure AI is seeking a Reinforcement Learning Engineer to develop and deploy advanced RL algorithms for controlling their humanoid robots' whole body movements. You'll optimize sim-to-real performance, define evaluation metrics, and ensure robust control systems for general-purpose robotic tasks.

What you'll do

• Develop, train, and deploy reinforcement learning algorithms for whole body robot control
• Design optimal observation and action spaces and select appropriate model architectures
• Identify and resolve simulation-to-reality performance gaps
• Define, test, and evaluate performance metrics for learned policies
• Ensure robustness and reliability of the control stack
• Lead complex control projects and mentor junior engineers

What they're looking for

• Reinforcement learning algorithms (PPO, SAC, etc.)
• Dynamics and control theory, preferably legged robotics
• Hyperparameter tuning and cost function optimization
• Domain randomization, curriculum learning, reward shaping
• Behavior cloning and distillation techniques
• Project leadership and technical mentoring
• Python or similar programming languages
• Robotics simulation and control systems

Benefits

• Competitive salary: $200,000–$300,000 annually
• Full-time position with comprehensive total compensation package
• 5 days/week in-office collaboration in North San Jose, CA
• Work on cutting-edge humanoid robotics with human-level intelligence goals