This role focuses on defining and advancing learning-based approaches for motion planning in autonomous systems. It involves setting the technical direction and delivering solutions that evolve from traditional rule-based and optimization-driven methods toward data-driven techniques such as imitation learning, reinforcement learning, and diffusion-based planning.
The position combines hands‑on technical leadership with ownership of the roadmap, ensuring that learning-based planners are not only innovative but also practical, reliable, and ready for real-world deployment. The emphasis is on delivering measurable improvements over existing planning approaches through robust engineering and evaluation.