2027 Summer Intern, PhD, Research, AV Planning

Design, implement, and benchmark a system where LLM meta-agents generate, synthesize, and iteratively refine reward functions for autonomous driving planning policies

Diagnose policy failure modes, reward exploitation, and multi-objective trade-offs, integrating findings back into the automated discovery loop

Collaborate with scientists engineers to transfer validated reward formulations and tooling into planner recipes

Document research outcomes, distill key design patterns, and prepare findings for internal tech transfers and potential conference publication

Currently pursuing a PhD in Computer Science, Robotics, Machine Learning, or a related quantitative field

Strong hands-on experience in Reinforcement Learning (RL / RLFT) and training deep learning policies with PyTorch or JAX

Demonstrated proficiency in Python and modern ML engineering practices

Familiarity with autonomous vehicle planning, trajectory optimization, or sequential decision-making under uncertainty

Track record of peer-reviewed publications at top AI/robotics conferences (e.g., NeurIPS, ICRA, IROS, CoRL, ICML, ICLR, CVPR)

Experience with LLM-guided code generation, automated reward discovery (e.g., Eureka-like frameworks), or meta-RL methods

Hands-on experience with RL or RLFT training in LLM/VLM/robotics

Proven ability to independently drive an exploratory research problem from ideation to implementation and results

Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in.

The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements.

Hourly PhD Pay

$85 — $85 USD