Helix AI Engineer, Reinforcement Learning

Figure; San Jose (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

A leading AI robotics company in San Jose, CA is seeking a Helix AI Engineer specializing in reinforcement learning. The role focuses on developing learning systems that enable humanoid robots to acquire skills through interaction and feedback. Candidates should have experience in RL algorithms, proficiency in Python, and strong software engineering skills. This position requires 5 days a week in-office collaboration and offers the opportunity to work at the forefront of AI technology.

Qualifications

• Experience developing and applying RL algorithms in complex environments.
• Strong understanding of RL fundamentals (policy optimization, value methods).
• Experience training policies in real-world systems.

Responsibilities

• Design and implement reinforcement learning algorithms for embodied agents.
• Train policies that learn from interaction and large-scale experience.
• Develop reward modeling and exploration strategies for long-horizon behaviors.

Skills

Reinforcement Learning algorithms

Python

Deep Learning frameworks (e.g., PyTorch)

Software engineering skills

Job description

San Jose, CA

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 systems
• Strong 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.