• 3+ years working on robots in industry or research, with shipped work to show for it: a track record, not just theory.
• Hands-on experience with modern manipulation methods, ideally close to production deployment.
• Solid understanding of modern teleoperation and low-level robot control stacks.
• Experience with neural network post-training: behaviour cloning and RL applied to physical systems.
• Familiarity with deep learning infrastructure: streaming datasets, checkpointing, distributed training, PyTorch or JAX. You can profile and debug numerics.
• Strong software engineering and coding fundamentals: maintainable research code, clear experiment docs, crisp communication of trade-offs. The process includes a LeetCode-style coding round, so come prepared.
• You are driven by getting things to work on the robot, not by writing the perfect algorithm in simulation. When something breaks, your instinct is to debug it, not publish it.
• You are proactive: you can take an open problem and drive it without waiting for a spec.
• Working English.
• Slightly below the experience bar but current on modern manipulation methods? Apply anyway. Strong PhD students close to finishing and postdocs are welcome too.
• Experience training VLA models for manipulation (autoregressive, diffusion or flow-matching based); familiarity with OpenVLA and Physical Intelligence (π) models.
• Experience applying RL to robotic problems.
• PhD or postdoc from a top robotics or ML program.
• Publications at top-tier robotics or DL conferences, or equivalent open-source contributions.