Helix AI Engineer, Reinforcement Learning

Figure; San Jose (CA); On-site; CAD 120,000 - 180,000; Full time

Job summary

Figure, an AI robotics company, seeks a Helix AI Engineer, Reinforcement Learning, to advance embodied autonomy for humanoid robots. The role emphasizes applying RL to both simulated and real-world environments and collaborating across teams to improve policy performance and robustness.

You will work on scalable RL training systems, evaluation frameworks, and cross-team integration to push long-horizon decision-making in embodied systems. This is a senior, in-office role centered in Canada.

Qualifications

• Experience developing and applying reinforcement learning algorithms in complex environments.
• Proficiency in Python and PyTorch.
• Experience with large-scale experimentation and distributed training.

Responsibilities

• Design and implement reinforcement learning algorithms for embodied agents in real-world and simulated environments.
• Train policies from interaction, feedback, and large-scale experience across diverse tasks.
• Develop reward modeling and exploration strategies for long-horizon behaviors.

Skills

Reinforcement learning

Python

PyTorch

RL fundamentals

Distributed training

Robotics

Software engineering

Job description

Figure is an AI robotics company developing autonomous general-purpose humanoid robots. Our goal is to build embodied AI systems that can perceive, reason, and act in the real world. Figure is headquartered in San Jose, CA, and this role requires 5 days/week in-office collaboration.

Our Helix team is responsible for developing the core AI systems that power humanoid autonomy. We are looking for a Helix AI Engineer, Reinforcement Learning to develop learning systems that enable robots to acquire skills through interaction, feedback, and experience.

This role 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.

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 complex, long-horizon behaviors
• Improve policy robustness to real-world challenges such as noise, partial observability, and environment variability
• Work across online and offline RL settings, including learning from large-scale logged robot data
• Collaborate closely with pretraining, video, generative, agent, and robot learning teams to integrate RL into the full autonomy stack
• Build scalable training systems for RL, including distributed rollouts, simulation infrastructure, and experiment management
• Design evaluation frameworks to measure policy performance, stability, and generalization

Requirements

• Experience developing and applying reinforcement learning algorithms in complex environments
• Strong understanding of RL fundamentals (e.g., policy optimization, value methods, model-based RL)
• Experience training policies in simulation and/or real-world systems
• Proficiency in Python and deep learning frameworks such as PyTorch
• Experience with large-scale experimentation and distributed training systemsStrong experimental rigor and ability to diagnose and improve learning systems
• Solid software engineering skills and ability to build scalable, reliable systems
• Ability to operate independently and drive ambiguous, high-impact technical problems

Bonus Qualifications

• Experience applying RL to robotics, control systems, or embodied AI
• Experience with large-scale RL infrastructure (distributed rollouts, simulation at scale)
• Background in offline RL, imitation learning, or hybrid learning approaches
• Experience with reward modeling or human-in-the-loop learning
• Experience at leading AI labs such as OpenAI, Google DeepMind, Anthropic, or xAI
• Familiarity with robotics systems, simulation environments, or real-world deployment constraints
• Publication record in reinforcement learning, machine learning, or robotics

The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.