Figure
Helix AI Engineer, Reinforcement Learning
About this role
Figure AI is seeking a Reinforcement Learning Engineer to develop core AI systems that enable humanoid robots to learn and perform complex tasks through interaction and feedback. You'll design and optimize RL algorithms for both simulated and real-world robotic environments, working closely with cross-functional teams to integrate learning into the full autonomy stack.
What you'll do
- Design and implement reinforcement learning algorithms for embodied agents in real-world and simulated environments
- Train policies using interaction, feedback, and large-scale experience data across diverse robotic tasks
- Develop reward modeling, credit assignment, and exploration strategies for long-horizon behaviors
- Improve policy robustness to real-world challenges like noise, partial observability, and environment variability
- Build scalable training systems including distributed rollouts, simulation infrastructure, and experiment management
- Collaborate with pretraining, vision, generative, and robotics teams to integrate RL into the autonomy stack
What they're looking for
- Reinforcement learning algorithms (policy optimization, value methods, model-based RL)
- Python and PyTorch
- Policy training in simulation and real-world systems
- Distributed training and large-scale experimentation
- Robotics and embodied AI systems
- Offline RL and imitation learning
- Software engineering and scalable system design
- Experimental validation and debugging
Benefits
- Work on cutting-edge humanoid robotics technology
- Collaborate with leading AI researchers and cross-functional teams
- Access to large-scale simulation and real robot systems
- Full-time in-office role in San Jose, CA with 5 days/week collaboration
- Competitive compensation based on experience
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Figure
Figure develops advanced humanoid robots powered by AI technology. The company is hiring engineers across mechanical design, firmware development, manufacturing, quality assurance, and security to build and refine its autonomous robotic systems.
View all jobs at FigureLikely interview questions
- Walk us through a reinforcement learning project where you trained a policy in simulation and deployed it to a real-world system. What sim-to-real gaps did you encounter and how did you address them?
- Describe your experience with reward modeling and credit assignment in long-horizon tasks. How do you approach designing rewards for complex behaviors that take many steps to complete?