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Reinforcement Learning Engineer – 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 engine

Job Full source details
Figure San Jose, San Jose, CA, United States Verified 16 hours ago
✓ 100% verification score · Source: Figure Careers · Always confirm final requirements on the original source.
Complete source information imported The available role or programme description, requirements, benefits and source facts were imported from the public official endpoint and formatted for reading.
DeadlineOpen on the verified official source at review time; no explicit closing date was captured.
EmploymentFull-time
CountryUnited States

Overview

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 engine

Full job description

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 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 $150,000 and $350,000 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. 

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Verification notes

Verified current on Figure's official Greenhouse posting on 28 August 2026; title, San Jose location, source ID and application destination checked.

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