2027 Summer Intern, PhD, Research, RSI for Autonomous Driving

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.

The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.

Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship!

You will:

• Literature research of SOTA in RSI, physics AI foundational models, autonomous driving foundational models, World Action models and connect the SOTA to their research projects to improve driver quality

• Design new data and post-training recipe to RL the driver with the critic model and world model in a close loop setup

• Coding and implementing the RSI system

• Inspect training results and ablation studies to iterate the algorithm design

You have:

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

• Depth in foundational AI model post-training, especially RL

• Fluency in using AI coding agents, fluency in one of the RL training framework such as Rex, Verl, NeMo-RL

• Fluency in AI model training basics such as transformer architecture, hyperparameter tuning, overfitting debug

• Familiarity with autonomous driving, physics AI, VLA, or world models

We prefer:

• Depth in RL for robotics or autonomous driving models

• Depth in world action models or video generation models

• Experience in Recursive Self-Improvement training for AI models.

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