Senior Reinforcement Learning Engineer

Our client is building robots for the real world to improve human quality of life and to help solve the ever‑increasing labor shortage problem. Their team has been building some of the most advanced robots on the planet for years, developing general‑purpose robots designed to operate in human spaces and with human tools.

The Senior Reinforcement Learning Engineer is a key, hands‑on role focused on improving performance of humanoid robots. You will leverage your deep expertise in RL to solve critical locomotion and manipulation challenges and deliver breakthrough results on physical hardware. The primary focus of this role is to rapidly implement, iterate, and deploy advanced learning algorithms to push the boundaries of what our robots can do.

As a senior member of the team, this individual will also be responsible for mentoring junior engineers and elevating the team's overall technical capabilities through their guidance and expertise.

• Implement and deploy RL algorithms to achieve ambitious, world‑class performance on dynamic locomotion and manipulation tasks with physical hardware.
• Drive the entire development cycle, from prototyping in simulation to robustly transferring and fine‑tuning policies on the robot.
• Optimize and scale the RL training pipeline for faster iteration, contributing to core infrastructure for high‑throughput simulation and distributed training.
• Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.
• Collaborate closely with the robotics and hardware teams to diagnose system‑level issues and co‑develop solutions that enable more complex learned behaviors.
• Analyze and present hardware results to guide future technical directions and demonstrate progress on key company objectives.

• Deep, hands‑on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high‑fidelity physics simulators (e.g., Mu Jo Co , Isaac Gym)
• Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code.
• Experience building or utilizing large‑scale, distributed training pipelines and a strong intuition for their optimization.
• A strong theoretical understanding of modern reinforcement learning, including deep expertise in areas like imitation learning, model‑based RL, and sim‑to‑real transfer techniques.
• A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning‑based approaches.
• A results‑oriented mindset with a passion for seeing complex algorithms work on real‑world hardware.

• A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred.
• A proven track record of successfully deploying learning‑based policies on physical robotic systems, especially legged robots or manipulators.
• Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment.
• A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus.

Candidate experience is important to us. This is a real job. We do not use AI to screen applications or to interview. Your application will be read and assessed by a human. We respond to all applications.

Mid‑Senior level

Full‑time