2027 Summer Intern, PhD

Responsibilities

• Design, implement, and scale reinforcement learning post-training and distillation algorithms for foundation world models in simulation and autonomous driving.
• Develop learned reward models, realism critics, and discriminators in a closed-loop multi-agent simulator.
• Run closed-loop ablation studies on large-scale TPU clusters using autonomous-driving behavior metrics.
• Investigate simulator exploitation and distribution shift through agentic auto-research workflows and fleet-scale analysis.
• Write clean, modular, and well-tested JAX code for simulation and autonomous-driving codebases.
• Collaborate with researchers and engineers across simulation, world modeling, and autonomous-driving teams.

Requirements

• Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, or a related technical field.
• Expected PhD graduation between December 2027 and August 2028, with at least one academic term remaining after the internship.
• Strong tensor implementation and debugging skills in Python and JAX/Flax or PyTorch.
• Hands-on experience training and modifying modern neural architectures such as Transformers, diffusion or flow models, or deep reinforcement learning networks.
• Research experience in reinforcement learning and post-training or generative world, sequence, or trajectory modeling, with working familiarity in the other area.
• Preferred: first-author publications at top-tier machine learning or robotics venues such as NeurIPS, ICML, ICLR, CVPR, CoRL, RSS, or ICRA.
• Preferred: experience with multi-agent closed-loop simulation, Sim2Real transfer, reward-hacking mitigation, or tools and environments such as Waymax, WOMD, NuPlan, IsaacGym, or MuJoCo.
• Preferred: experience profiling and scaling distributed training on TPUs or multi-node GPUs, including XLA compilation, vectorized batched rollouts, and memory optimization.
• Preferred: familiarity with C++/Python interfaces.

Benefits

• Hybrid onsite internship position.
• Full-time internship with an expected hourly PhD pay rate of $85 per hour.
• Interns are eligible for the company’s benefits programs, subject to eligibility requirements.
• Resumes are accepted on a rolling basis until the role is filled.