AI Training Infrastructure Engineer – Humanoid Whole Body Control

Figure is an AI Robotics company autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. We are based in North San Jose, CA and require 5 days/week in-office collaboration. It’s time to build.

We’re looking for an engineer to own the training and deployment backbone behind our RL-based whole-body control systems. This role sits at the intersection of robotics, machine learning, controls, and software systems engineering, and is critical to how quickly we can iterate, train, and deploy new capability to our fleet of humanoid robots.

Key Responsibilities:

Own and scale the infrastructure used to train whole-body control policies (simulation, data pipelines, orchestration, visualizations)

Design systems that are fast, reliable, and highly configurable for our controls engineers

Ensure high cluster utilization and minimal downtime—unblocking the team and accelerating iteration cycles

Evaluate and integrate physics engines, simulation environments, and parameterizations to balance realism and training speed

Optimize hyperparameters and infrastructure to maximize training speed and efficiency and final model performance

Build robust tooling to take policies from training → validation → deployment on hardware

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

Strong ownership mindset—you own systems that your teammates rely on every day

Experience modeling contact interactions and photorealistic simulation environments for complex manipulation tasks

Bonus 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

The US base salary range for this full-time position is between $150,000 and $300,000 annually.

The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.