Reinforcement Learning Engineer, Whole Body Control

United States·San Joselead

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Quick Summary

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 engineered to perform a variety of tasks in the home and commercial markets.

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…

Requirements Summary

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…

Technical Tools

mentoring

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

~1 min read

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Develop, train, and deploy reinforcement learning algorithms for whole body control.
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Determine the observations, actions, and model types that unlock maximum performance.
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Identify and close the most important sim-to-real gaps.
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Define, test, and evaluate performance metrics for learned policies.
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Harden the control stack to ensure rock solid robustness.

Requirements

~1 min read

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.

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.

Location & Eligibility

Where is the job

San Jose, United States

On-site at the office

Who can apply

Listed under

United States

About this job

Pay per year: $150,000–$350,000 a year; 254% above the $70,554 median for Other jobs on Jobera (110,457 salaries).
Who can apply: United States
Posted: 190 days ago
Employer: has 98 open jobs on Jobera in 3 countries.