Research Engineer / Scientist (Robot Learning)

Job description

Job responsibilities

• Design and implement modern robot learning systems, including imitation learning, reinforcement learning for manipulation.
• Research, prototype, and productionize robotic policies with a focus on speed, precision and scalability.
• Develop and improve training pipelines for sim-to-real transfer, including domain randomization, system identification, real-sim alignment.
• Collaborate with simulation and infrastructure teams to minimize sim-to-real gap and ensure learning methods integrate cleanly with real-robot deployment stacks.
• Build end-to-end training and evaluation workflows for robot policies, from large-scale data generation to scale up training and evaluation.
• Optimize policy performance across the stack, including training speed, inference latency, data generation efficiency to support large scale production constraints.
• Diagnose failure modes in simulation and real-world rollouts, design principled solutions to improve robustness, efficiency and generalization.
• Contribute to technical direction by proposing new research ideas, mentoring teammates, and helping set best practices for robot learning across the organization.

Job requirements

• PhD in robotics, machine learning, computer science, or a closely related field is strongly preferred.
• 6+ years of experience working on manipulation, locomotion, robot policy training, or related areas.
• Strong foundation in robotics, neural network designs, sim-real transfer.
• Deep experience with robot policy designs (e.g., VLA, WAM, diffusion).
• Proficiency in Python and/or C++, with hands-on experience building research or production robotic systems.
• Experience with deep learning frameworks (e.g., PyTorch) and low-level robotic controller.
• Proven ability to work in ambiguous, fast-moving environments and drive projects from concept through deployment.
• A strong sense of ownership and engineering rigor: you care deeply about correctness, stability, and measurable improvements.