You will:- 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
You have:- 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
We prefer:- 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
