2027 Summer Intern, PhD, Research, RSI for Autonomous Driving
About The Position
Waymo’s AI Foundations team develops machine learning solutions for open problems in autonomous driving, with the goal of safely operating Waymo vehicles in many cities and under all driving conditions. The team also initiates and fosters collaborations with other research teams in Alphabet. Current areas of focus include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. As a Research intern focused on RSI for autonomous driving, you will research the state of the art in RSI, physics AI foundational models, autonomous-driving foundational models, and World Action models, and connect that research to projects that improve driver quality. You will design data and post-training recipes to train the driver with reinforcement learning using critic and world models in a closed-loop setup, implement the RSI system, and inspect training results and ablation studies to refine the algorithm design. The internship is hybrid onsite. Applications are accepted on a rolling basis until the role is filled.
Requirements
- Currently pursuing a PhD in Computer Science, Robotics, Machine Learning, or a related quantitative field.
- Depth in foundational AI model post-training, especially reinforcement learning.
- Fluency in using AI coding agents and in at least one reinforcement learning training framework, such as Rex, Verl, or NeMo-RL.
- Fluency in AI model training basics, including transformer architecture, hyperparameter tuning, and overfitting debugging.
- Familiarity with autonomous driving, physics AI, VLA, or world models.
Nice To Haves
- Depth in reinforcement learning for robotics or autonomous-driving models.
- Depth in World Action models or video-generation models.
- Experience in Recursive Self-Improvement training for AI models.
Responsibilities
- Research the state of the art in RSI, physics AI foundational models, autonomous-driving foundational models, and World Action models, and connect it to research projects to improve driver quality.
- Design data and post-training recipes to train the driver with reinforcement learning using critic and world models in a closed-loop setup.
- Code and implement the RSI system.
- Inspect training results and ablation studies to iterate on algorithm design.
Benefits
- Eligible to participate in the Company’s benefits programs, subject to eligibility requirements.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
