Embodied RL Engineer for Humanoid Autonomy

Figure; California; On-site; USD 140,000 - 230,000; Full time

Job summary

Figure, a San Jose-based AI robotics company, seeks a Helix AI Engineer, Reinforcement Learning to advance learning systems for embodied autonomy. This role focuses on RL across simulation and real-world robotics, shaping policies for long-horizon decisions.

You will design RL algorithms, train robust policies, and build scalable training systems while collaborating with cross-functional teams to integrate RL into the full autonomy stack.

Qualifications

• Experience applying reinforcement learning to complex environments.
• Strong knowledge of RL fundamentals: policy optimization, value methods, model-based RL.
• Experience training policies in simulation and real-world systems.
• Proficiency in Python and PyTorch for ML and RL workflows.
• Experience with large-scale experimentation and distributed training.
• Strong experimental rigor and ability to diagnose learning systems.
• Solid software engineering skills and scalable, reliable systems.

Responsibilities

• Design and implement reinforcement learning algorithms for embodied agents operating in real-world and simulated environments.
• Train policies that learn from interaction, feedback, and large-scale experience across diverse tasks.
• Develop reward modeling, credit assignment, and exploration strategies for long-horizon behaviors.
• Improve policy robustness to real-world challenges such as noise and partial observability.
• Work across online and offline RL settings, including learning from large-scale logged robot data.
• Collaborate with pretraining, video, generative, agent, and robot learning teams to integrate RL into the autonomy stack.
• Build scalable training systems for RL, including distributed rollouts and simulation infrastructure.
• Design evaluation frameworks to measure policy performance, stability, and generalization.

Skills

Reinforcement Learning

Python

PyTorch

Distributed Training

Robotics/Embodied AI

RL in real-world

Experimentation & Diagnostics

Tools

ROS

Simulation Environments

Distributed Systems

Job description

Figure, a San Jose-based AI robotics company, seeks a Helix AI Engineer, Reinforcement Learning to advance learning systems for embodied autonomy. This role focuses on RL across simulation and real-world robotics, shaping policies for long-horizon decisions.

You will design RL algorithms, train robust policies, and build scalable training systems while collaborating with cross-functional teams to integrate RL into the full autonomy stack.