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

What you'll do

• Design and scale reinforcement-learning post-training and distillation algorithms for simulation and autonomous driving.
• Develop learned reward models, realism critics, and discriminators for a closed-loop multi-agent simulator.
• Run large-scale closed-loop studies and write tested JAX code for simulation and autonomous driving systems.

What they ask for

• Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, or a related technical field; anticipated graduation December 2027–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, with hands-on experience training and modifying modern neural architectures.
• Hands-on research expertise in either reinforcement learning and post-training or generative world, sequence, or trajectory modeling, with working familiarity in the other.

Nice to have

• First-author publications at top-tier machine learning or robotics venues in relevant fields.
• Experience with multi-agent simulation, Sim2Real transfer, reward-hacking mitigation, or distributed training on TPUs or multi-node GPUs.

Pay

$85 USD per hour (hourly PhD pay)

Tools

Python, JAX, Flax, PyTorch, TPU, C++, Waymax, WOMD

AI focus

Reinforcement learning post-training and distillation, generative world and trajectory modeling, learned rewards, and closed-loop simulation for autonomous driving.