AI Training Infrastructure Engineer

Responsibilities

• Own and scale the infrastructure used to train whole-body control policies, including simulation, data pipelines, orchestration, and visualizations
• Design fast, reliable, and highly configurable systems for controls engineers
• Ensure high cluster utilization and minimal downtime to accelerate iteration cycles
• Evaluate and integrate physics engines and simulation environments to balance realism and training speed
• Optimize hyperparameters and infrastructure to maximize training efficiency and model performance
• Build robust tooling to transition policies from training and validation to hardware deployment

Requirements

• Strong software engineering fundamentals with production experience in Python and PyTorch
• Experience building or scaling training infrastructure for robotics, control systems, or large-scale ML workloads
• Familiarity with physics simulation tools such as NVIDIA PhysX, MuJoCo, Warp, or PyBullet
• Working knowledge of dynamics, controls, and robotics systems
• Experience with reinforcement learning, imitation learning, or policy distillation
• Experience modeling contact interactions and photorealistic simulation environments for complex manipulation tasks
• Strong ownership mindset for mission-critical systems

Preferred Qualifications

• Experience with humanoid or legged robot control
• Background in distributed systems, job schedulers, or cluster management
• Experience deploying ML models or control policies to real-world systems

About the Company

Figure is an AI Robotics company building autonomous general-purpose humanoid robots. Our goal is to ship humanoid robots with human-level intelligence designed to perform a variety of tasks in home and commercial markets. We are based in North San Jose, CA.