AI Training Infrastructure Engineer – Humanoid Whole Body Control

About this role

Figure AI seeks an AI Training Infrastructure Engineer to build and scale the training and deployment systems for reinforcement learning-based whole-body control policies on humanoid robots. You'll own the simulation, data pipelines, and orchestration infrastructure that enables rapid iteration and deployment of robot capabilities across the fleet.

What you'll do

• Own and scale training infrastructure for whole-body control policies including simulation, data pipelines, and orchestration
• Design fast, reliable, and configurable systems for controls engineers to train policies efficiently
• Optimize cluster utilization and minimize downtime to accelerate team iteration cycles
• Evaluate and integrate physics engines and simulation environments to balance realism with training speed
• Build robust tooling for policy validation and deployment from training to real hardware
• Optimize hyperparameters and infrastructure to maximize training efficiency and model performance

What they're looking for

• Python and PyTorch production experience
• Physics simulation tools (PhysX, MuJoCo, Warp, PyBullet)
• Reinforcement learning and policy distillation
• Robotics dynamics and control systems
• ML training infrastructure and scaling
• Distributed systems and job scheduling
• Contact modeling and photorealistic simulation
• Hardware deployment of ML and control policies

Benefits

• Competitive salary range: $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 and AI technology
• Role with direct impact on robot capability scaling