# Reinforcement Learning Engineer, Whole Body Control at Figure

Reinforcement Learning Engineer, Whole Body Control at Figure in San Jose, CA. Full-time, paying $150k–$350k. Posted April 8, 2026.

- Company: Figure
- Location: San Jose, CA
- Job type: Full-time
- Pay: $150k–$350k
- Posted: 2026-04-08
- Apply: https://job-boards.greenhouse.io/figureai/jobs/4671442006
- Source: https://jobmagic.ai/jobs/reinforcement-learning-engineer-whole-body-control-figure-geeter

## 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.

## Description

Figure is an AI robotics company developing 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. Figure is headquartered in San Jose, CA.
We are looking for a Reinforcement Learning Engineer to develop, train, deploy, and evaluate advanced reinforcement learning algorithms for whole body control of our humanoid robot.

**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 $150,000 to $350,000 per year.

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.