Helix AI Engineer, Reinforcement Learning
figureSan Jose (CA)
Helix AI Engineer, Reinforcement Learning
Posted 2 days ago
figureSan Jose (CA)
SENIORITY
Lead
About the role
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 PyTorchExperience 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 AIExperience 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 xAIFamiliarity 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.
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.