Helix AI Engineer, Reinforcement Learning
Figureai•5h ago
United StatesOnsite$200K–$400KFull-timeMid Level3+ yrs exp
Top focus
Ai EngineerRl EngineerDeep Learning Engineer
- Figure is an AI robotics company developing autonomous general-purpose humanoid robots. Our goal is to build embodied AI systems that can perceive, reason
- act in the real world. Figure is headquartered in San Jose, CA
- 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 US base salary range for this full-time position is between $200,000 - $400,000
- The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills
- 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.
Required skills
Pythonreinforcement learningdeep learningPyTorch