Helix Ai Engineer, Reinforcement Learning

Figureai

San Jose, CA, United States
On-site
Reinforcement learning algorithms
Policy optimization, value methods
Python and pytorch
Develop learning systems that enable robots to acquire skills through interaction, feedback, and experience

Job Summary

  • Develop learning systems that enable robots to acquire skills through interaction, feedback, and experience.
  • Focuses on applying and advancing reinforcement learning across simulation and real-world environments—improving policy performance, robustness, and long-horizon decision-making in embodied systems.
  • Collaborate closely with pretraining, video, generative, agent, and robot learning teams to integrate RL into the full autonomy stack.

Matching Summary

Develop learning systems that enable robots to acquire skills through interaction, feedback, and experience.

Skills & Requirements

Must-have

  • Reinforcement learning algorithms
  • Policy optimization, value methods
  • Python and PyTorch
  • Large-scale experimentation
  • Scalable, reliable systems

Nice-to-have

  • RL applied to robotics
  • Large-scale RL infrastructure
  • Offline RL, imitation learning
  • Reward modeling, human-in-the-loop
  • Publication record in RL

Key Requirements

  • Experience developing RL algorithms
  • Proficiency in Python and PyTorch
  • Experience with large-scale experimentation
  • Solid software engineering skills

Work Rights

Not specified

Tailored Resume

Cover Letter