Reinforcement Learning Engineer, Whole Body Control
At a glance
Mid-level Machine Learning Engineering role at Figure. San Jose · $150,000–$350,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build, train and put reinforcement learning methods into use for whole-body humanoid control
- Choose the sensors, control outputs and model designs that improve policy performance
- Find simulation-to-robot differences that matter most, then work to reduce them
- Set measures for learned policies and test their results
- Strengthen the control stack so it remains robust
What you bring
- You have a strong grasp of dynamics and control, preferably involving legged robots
- You have applied robotics reinforcement learning methods such as PPO or SAC
- You have tuned hyperparameters and cost functions for reinforcement learning algorithms
- You can lead complex controls work and guide less experienced engineers
Who this fits
This role suits an engineer who can lead controls projects and mentor junior engineers. The position is based in San Jose, California.
From the employer
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:
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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:
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Strong background in dynamics and control, ideally of legged robots.
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Experience with reinforcement learning algorithms for robotics: PPO, SAC, etc.
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Experience tuning hyperparameters and cost functions for these RL algorithms.
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Familiarity with common RL techniques such as: domain randomization, curriculum learning, reward shaping, etc.
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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 $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.