2026 Summer Intern, PhD, Data Science
Waymo
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.
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:
- Analyze and extract complex data from large tables, exploring predictive features.
- Develop tools for analyzing simulation data, partnering with engineering teams, and leveraging foundation models for workflow automation.
- Adapt state-of-the-art ML models for synthetic scene rendering and sensor data generation, integrating generated data into the Waymo simulation platform, conducting experiments to evaluate quality, and developing new sampling methods.
You Have:
- Pursuing PhD in a quantitative field (e.g. Statistics, Computer Science, Mathematics, Physics, Engineering)
- Proficiency in programming with SQL and Python (or R), including data manipulation, wrangling, and visualization.
- Strong statistical theory foundation.
We Prefer:
- Experience in machine learning applications and complex data analysis, or familiarity with C/C++.
- Experienced in processing and interpreting various sensor data
- Demonstrated research capabilities through publications and a strong understanding of statistical methodologies and foundation models.
