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Reinforcement Learning Engineer

Fovix Labs

Hybrid · $200k+ / year · Full-time · Posted 5 months ago

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About the role

We're building autonomous decision systems for industrial robotics. Not warehouse pick-and-place — complex assembly tasks where a robot needs to figure out the right grasp, force, and sequence for objects it's never seen before. Our RL agents train in simulation and deploy on physical hardware. You'll lead the RL research and engineering for our manipulation planning module. That means designing reward functions, building simulation environments, training policies in Isaac Gym, and validating them on real robots in our LA lab. The sim-to-real gap is the hard part, and we need someone who's crossed it before. This is a hybrid role — 3 days/week in our Culver City lab because robot debugging doesn't work over Zoom. You'll have access to a fleet of robot arms, a dedicated GPU cluster for training, and a team of 4 engineers who are as obsessed with this problem as you are.

Responsibilities

  • Design reward functions and training curricula for manipulation tasks
  • Build and maintain simulation environments for RL policy training
  • Bridge the sim-to-real gap through domain randomization and adaptation
  • Validate trained policies on physical robot hardware in the lab
  • Lead technical direction for the RL manipulation planning module

Requirements

  • 5+ years in reinforcement learning with sim-to-real deployment experience
  • Deep PyTorch expertise and strong software engineering skills
  • Experience with robotics simulation (Isaac Gym, MuJoCo, or similar)
  • Published research in RL or robotics preferred
  • Willingness to work on-site 3 days/week in Culver City, CA

Benefits

  • Hybrid — Culver City lab 3 days/week
  • $200k+ base plus equity
  • Dedicated GPU cluster for research
  • Relocation assistance available
  • Annual robotics conference attendance

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Categories

AI & Machine Learning Engineers

Skills

python pytorch reinforcement learning docker aws

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Salary Range

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AI Expertise

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