2027 Summer PhD Research Intern, Multimodal Foundation Models

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: • Run data mixture ablations and fit scaling laws • Research efficient data curriculum for various Foundation modeling tasks • Research contribution and efficiency of different modalities in a multi-modal Foundation Model • Explore and apply training data attribution (TDA) and data valuation methods to quantify dataset utility and guide data curation for foundation models You have: • Currently pursuing a PhD in Computer Science, Robotics, Machine Learning, or a related quantitative field • In-depth knowledge of state of the art foundation models.

• Experience with data pipelines at scale • Strong foundation in machine learning, deep learning, and statistical modeling/optimization • Proficiency in Python and modern deep learning frameworks (e.

g., JAX, PyTorch) for training and evaluating large-scale neural networks We prefer: • Experience with Grain • Research or practical experience with neural scaling laws, data mixture optimization, curriculum learning, or training data attribution 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.