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 $200,000 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. 

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 are looking for a Staff Reinforcement Learning Engineer to develop, train, deploy, and evaluate advanced reinforcement learning algorithms for whole body control of our humanoid robot.

Key Responsibilities:

• Develop, train, and deploy reinforcement learning algorithms for whole body control
• Determine the observations, actions, and model types that unlock maximum performance
• Identify and close the most important sim-to-real gaps
• Define, test, and evaluate performance metrics for learned policies
• Harden the control stack to ensure rock solid robustness

Requirements:

• Strong background in dynamics and control, ideally of legged robots
• Experience with reinforcement learning algorithms for robotics: PPO, SAC, etc
• Experience tuning hyperparameters and cost functions for these RL algorithms
• Familiarity with common RL techniques such as: domain randomization, curriculum learning, reward shaping, etc.
• Capable of leading complex controls projects and mentoring junior engineers

Bonus Qualifications:

• Experience with behavior cloning techniques (e.g. distillation)

The US base salary range for this full-time position is between $200,000 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.