2027 Summer Intern, PhD, Machine Learning - Simulation & Closed-Loop Training

Highlights

• Scale reinforcement learning post-training for foundation world models.
• Build reward models and critics to align simulated driving with human realism.
• Run closed-loop studies with fleet-scale metrics to reduce simulator exploitation.

Ideal candidate

A curious PhD researcher passionate about autonomous driving and realistic simulation who loves rigorous experimentation and collaborating across teams to improve safety.

Requirements

• Currently pursuing a PhD in Computer Science, Machine Learning, Robotics or related field, graduating Dec 2027-Aug 2028 and returning for at least one term after internship
• Strong Python and JAX/Flax or PyTorch implementation skills training modern neural architectures like Transformers, Diffusion/Flow models or deep RL networks
• Hands-on research in RL post-training or generative world, sequence or trajectory modeling with familiarity in the other area