Reinforcement Learning Engineer, Whole Body Control
- Pay
- $150,000 – $350,000 per year
- Location
- San Jose, CA · On-site
- Job type
- Full-time
- Level
- Senior
- Listed
- 30 days ago
Skills & highlights for this role
- reinforcement learning
- legged robot dynamics and control
- PPO
- SAC
Also useful
- domain randomization
- curriculum learning
- behavior cloning
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).
Pay and benefits
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.
About Figure
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.
From Figure's careers page, first listed on Rolev.ai on October 10, 2026. Rolev.ai is not the employer.
How Rolev.ai helps with this application
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Common questions
What is the salary for the Reinforcement Learning Engineer, Whole Body Control role at Figure?
The Reinforcement Learning Engineer, Whole Body Control posting at Figure states compensation of $150,000 – $350,000 per year. It is listed as a full-time position. Final pay may depend on location, experience, and interview evaluation on Figure's official career site.
Where is the Reinforcement Learning Engineer, Whole Body Control job at Figure located?
This Reinforcement Learning Engineer, Whole Body Control position at Figure is located in San Jose, CA (On-site). Always check Figure's application page for specific state eligibility or relocation policies.
What qualifications are required for Reinforcement Learning Engineer, Whole Body Control at Figure?
For the Reinforcement Learning Engineer, Whole Body Control role, Figure highlights experience with reinforcement learning, legged robot dynamics and control, PPO, SAC. Rolev.ai can score your existing resume against these requirements and show any skill gaps before you apply.
How do I apply for the Reinforcement Learning Engineer, Whole Body Control position at Figure?
This listing links directly to Figure's Greenhouse application page. You can apply directly on Figure's site, or use Rolev.ai to check your resume fit, generate a zero-hallucination tailored resume and cover letter for this posting, and autofill the Greenhouse application form in 1 click with the Rolev.ai Chrome extension. You always review the form and press Submit yourself.