Reinforcement Learning Engineer

Hands-on expertise (3+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym).

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 degree in Computer Science, Robotics, or a related field, or an MS degree in a similar field with 2+ years industry experience.

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.

Prolonged periods of sitting at a desk and working on a computer

Vision to read printed materials and a computer screen

Hearing and speech to communicate

This is a direct hire. Please, no outside Agency solicitations.

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