Deep Research Expertise in Either (a) Reinforcement Learning & Post-Training (hands-on research experience in policy gradient methods such as PPO/GRPO/REINFORCE, off-policy or offline RL such as importance sampling/AWR/DPO, constrained MDPs, inverse RL, or learned reward and critic modeling) OR (b) Generative World, Sequence, or Trajectory Modeling (hands-on research experience in continuous flow matching / Rectified Flows, score-based diffusion policies such as DDPO/DPPO/Diffuser, or spatiotemporal autoregressive world and trajectory models), with working familiarity in the other. Run rigorous closed-loop ablation studies on large-scale TPU clusters-leveraging agentic auto-research workflows and fleet-scale driving behavior metrics (such as route progress, braking comfort, lateral stability, and lane selection)-to mitigate simulator exploitation and distribution shift.