Helix AI Engineer, Reinforcement Learning (AI)

TL;DR

Helix AI Engineer, Reinforcement Learning (AI): Developing learning systems that enable robots to acquire skills through interaction, feedback, and experience with an accent on improving policy performance, robustness, and long-horizon decision-making in embodied systems. Focus on applying and advancing reinforcement learning across simulation and real-world environments.

Location: Requires 5 days/week in-office collaboration in San Jose, CA

Company

hirify.global is an AI robotics company developing autonomous general-purpose humanoid robots.

What you will do

• 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.
• 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.

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.
• Solid software engineering skills and ability to build scalable, reliable systems.

Nice to have

• 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.
• Familiarity with robotics systems, simulation environments, or real-world deployment constraints.