Research Engineer / Scientist
Research Engineer/Scientist developing state-of-the-art robot-learning policies for manipulation, with emphasis on sim-to-real transfer, scalable training and inference, and robust real-world deployment. Requires 6+ years of relevant experience, strong robotics and deep-learning expertise, and Python or C++.
About the job
Responsibilities
- Design and implement modern robot-learning systems, including imitation learning and reinforcement learning for manipulation.
- Research, prototype, and productionize robotic policies focused on speed, precision, and scalability.
- Develop training pipelines for sim-to-real transfer, including domain randomization, system identification, and real-sim alignment.
- Collaborate with simulation and infrastructure teams to reduce the sim-to-real gap and integrate learning methods with real-robot deployment stacks.
- Build end-to-end training and evaluation workflows, from large-scale data generation through training and evaluation.
- Optimize training speed, inference latency, and data-generation efficiency for production-scale constraints.
- Diagnose simulation and real-world rollout failures and develop solutions that improve robustness, efficiency, and generalization.
- Contribute research ideas, mentor teammates, and establish best practices for robot learning.
Requirements
- 6+ years of experience in manipulation, locomotion, robot policy training, or related areas.
- Strong foundation in robotics, neural network design, and sim-to-real transfer.
- Deep experience with robot policy designs, such as VLA, WAM, or diffusion-based policies.
- Proficiency in Python and/or C++.
- Hands-on experience building research or production robotic systems.
- Experience with deep-learning frameworks such as PyTorch and low-level robotic controllers.
- Ability to drive projects from concept through deployment in ambiguous, fast-moving environments.
- Strong ownership, engineering rigor, and focus on correctness, stability, and measurable improvements.
- Collaborative approach and commitment to high-quality experimentation, design, and code.
Skills
Robotics, Imitation Learning, Reinforcement Learning, Robot Learning, Sim-To-Real Transfer, Domain Randomization, System Identification, Python, C++, PyTorch, Diffusion Models, Neural Networks, Robot Policies, Simulation, Low-Level Controllers
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