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