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Apptronik

Reinforcement Learning Engineer

Austin, TX · On-site

As a Reinforcement Learning Engineer, you will be a core contributor to the intelligence and physical capabilities of our humanoid platforms. This role is dedicated to architecting sophisticated ...

Apptronik

Reinforcement Learning Engineer

Austin, TX · On-site

As a Reinforcement Learning Engineer, you will be a core contributor to the intelligence and physical capabilities of our humanoid platforms. This role is dedicated to architecting sophisticated ...

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As of Aug 10, 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 August 2026, with employment types broken down into 75% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 93% Physical, 1% Hybrid, and 6% Remote job distribution, with an average salary of $34,368 per year, or $16.5 per hour.

Helix AI Engineer, Reinforcement Learning

Figure

San Jose, CA • On-site

Full-time

Re-posted 2 days ago

Job description

Job Summary:
Figure is an AI robotics company developing autonomous general-purpose humanoid robots. They are seeking a Helix AI Engineer, Reinforcement Learning to develop learning systems that enable robots to acquire skills through interaction, feedback, and experience, focusing on reinforcement learning across simulation and real-world environments.
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
Qualifications:
Required:
• 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
Preferred:
• 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
Company:
Figure is an AI robotics company that develops autonomous general-purpose humanoid robots. Founded in 2022, the company is headquartered in San Jose, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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