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Reinforcement Learning Engineer – Whole Body Control

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Last seen by MeritLog September 12, 2026Source: GreenhouseSource version: greenhouse-job-board-v1

Source: the employer's Greenhouse job board. Open the original listing for current details. Availability is not verified for this retained page.

Job details

Work model: On-site
Salary: Conflicting source ranges
Location: San Jose, CA

What the role asks for

What you'd do

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

What they're asking for

Strong background in dynamics and control, ideally of legged robotsSkillPreferred
Experience with reinforcement learning algorithms for robotics: PPO, SAC, etcSkill
Experience tuning hyperparameters and cost functions for these RL algorithmsSkill
Familiarity with common RL techniques such as: domain randomization, curriculum learning, reward shaping, etc.Skill
Capable of leading complex controls projects and mentoring junior engineersSkill
Experience with behavior cloning techniques (e.g. distillation)SkillPreferred

Parsed by MeritLog from the employer’s own posting. The full description follows below.

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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