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Member of Engineering (Reinforcement Learning)

$99K - $136K/yr

ABOUT THE ROLE You would be working on our reinforcement learning team focused on improving reasoning and coding abilities of Large Language Models through reinforcement learning. This is a hands-on ...

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How much do reinforcement jobs pay per hour?

As of Jul 7, 2026, the average hourly pay for reinforcement in the United States is $16.52, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $15.87 per hour, depending on experience, location, and employer.
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Infographic showing various Reinforcement job openings in the United States as of July 2026, with employment types broken down into 74% Full Time, 24% Part Time, 1% Temporary, and 1% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $34,368 per year, or $16.5 per hour.
Helix AI Engineer, Reinforcement Learning

Helix AI Engineer, Reinforcement Learning

Figure

San Jose, CA • On-site, Remote

Other

Posted 29 days ago

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 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. 

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