Key Responsibilities

• Research and develop planning modules for E2E driving models, including trajectory scoring/selection, diffusion- or anchor-based trajectory generation, and multi-modal planning heads
• Design closed-loop training strategies (e.g., closed-loop fine-tuning, RL/IL hybrid post-training, world-model-based rollout training) to mitigate distribution shift and compounding errors
• Build and run closed-loop evaluation pipelines on benchmarks such as NAVSIM, Bench2Drive, CARLA Leaderboard, or nuPlan; analyze failure modes and drive iterative improvement
• Conduct ablations and large-scale experiments; profile and optimize training/inference throughput on multi-GPU clusters
• Collaborate with full-time researchers and engineers; present findings internally and contribute to publications and/or patents

Minimum Qualifications

• Currently enrolled in a PhD program in Robotics, Computer Science, Electrical Engineering, or a related field.
• Solid foundation in deep learning and strong proficiency in Python and PyTorch.
• Understanding of motion planning and/or policy learning fundamentals (imitation learning, reinforcement learning, or trajectory optimization).
• Experience training and debugging neural networks at scale.
• Ability to read, implement, and extend recent research papers independently.

Bonus Qualifications​