2027 Summer Intern, PhD, Research, AV Planning

What you'll do

• Design, implement, and benchmark the reward-function discovery system; diagnose policy failures, reward exploitation, and multi-objective trade-offs.
• Collaborate with scientists and engineers to transfer validated reward formulations and tools into planner recipes.
• Document research outcomes and prepare findings for internal technology transfer and potential conference publication.

What they ask for

• Currently pursuing a PhD in Computer Science, Robotics, Machine Learning, or a related quantitative field.
• Hands-on experience in Reinforcement Learning (RL / RLFT) and training deep learning policies with PyTorch or JAX.
• Proficiency in Python and modern ML engineering practices.
• Familiarity with autonomous vehicle planning, trajectory optimization, or sequential decision-making under uncertainty.

Nice to have

• Peer-reviewed publications at top AI/robotics conferences.
• Experience with LLM-guided code generation, automated reward discovery, meta-RL, or RL/RLFT training in LLM/VLM/robotics.

Pay

$85–$85 USD hourly pay (the posting describes this as hourly PhD pay; it does not specify base salary or total pay).

Tools

LLMs, PyTorch, JAX, Python, RL, RLFT, VLM

AI focus

Develop LLM meta-agents that generate and iteratively refine reward functions for autonomous driving planning; investigate reinforcement learning, automated reward discovery, and policy evaluation.