Reinforcement Learning Engineer – Whole Body Control

Figure; San Jose (CA); On-site; USD 150,000 - 250,000; Full time

Job summary

An AI Robotics company based in San Jose is seeking a Staff Reinforcement Learning Engineer. The ideal candidate will develop, train, and deploy advanced reinforcement learning algorithms for whole body control of humanoid robots. Strong knowledge of dynamics, control, and robotics is essential, along with the ability to lead projects and mentor junior engineers. The position requires in-office collaboration five days a week. The salary range for this role is $150,000 to $250,000 annually.

Qualifications

• Strong background in dynamics and control, ideally of legged robots.
• Experience with reinforcement learning algorithms for robotics like PPO, SAC.
• Capability of leading complex controls projects and mentoring junior engineers.

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 for rock solid robustness.

Skills

Reinforcement Learning

Dynamics and Control

Hyperparameters Tuning

Robot Control

Mentoring

Job description

Staff Reinforcement Learning Engineer – Whole Body Control

San Jose, CA

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 $150,000 and $250,000 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.