Senior Reinforcement Learning Engineer

Join our team as a Senior Reinforcement Learning Engineer, where you will play a crucial role in achieving state-of-the-art performance on our humanoid robots. You will leverage your deep expertise in reinforcement learning to solve critical locomotion and manipulation challenges, and deliver breakthrough results on physical hardware. As a senior member of the team, you will also mentor junior engineers and elevate the team's overall technical capabilities.

• Implement and deploy state-of-the-art reinforcement learning algorithms to achieve ambitious 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, while optimizing and scaling the RL training pipeline.
• Mentor junior engineers by providing technical guidance, conducting insightful code reviews, and sharing best practices in reinforcement learning and software development.

• Must be able to lift 15 pounds at times
• A strong intuition for robot dynamics and controls theory, with the ability to apply these principles to guide and constrain learning-based approaches
• Deep, hands-on expertise (5+ years) with common RL frameworks (e.g., PyTorch, JAX) and high-fidelity physics simulators (e.g., MuJoCo, IsaacGym)
• Hearing and speech to communicate
• A results-oriented mindset with a passion for seeing complex algorithms work on real-world hardware
• 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
• Experience building or utilizing large-scale, distributed training pipelines and a strong intuition for their optimization
• Prolonged periods of sitting at a desk and working on a computer
• A PhD or MS in Computer Science, Robotics, or a related field, with 2+ years industry experience strongly preferred
• A strong publication record in relevant conferences or journals (e.g., CoRL, RSS, ICRA) is a significant plus
• Mastery of Python for rapid prototyping and training, alongside strong proficiency in C++ for developing performant, deployable code
• Demonstrated experience mentoring or providing technical guidance to other engineers in a team environment
• Vision to read printed materials and a computer screen
• A proven track record of successfully deploying learning-based policies on physical robotic systems, especially legged robots or manipulators