2027 Summer Intern, PhD, Data Science

Data Scientists at Waymo work on a wide variety of evaluation and modeling problems, including safety evaluation, the simulation technology, driving quality and rider experience. Furthermore, data scientists conduct rigorous analysis of the Waymo Driver performance across software releases, new territories, hardware generations, and throughout the software development process. This role works with a variety of teams across the company to inject data science rigor and perspective, and to influence development direction in many of the critical systems in self-driving technology and fleet operations.

As a Data Science intern, you may join one of these Data Science Subteams:

• World Model Eval
• Safety Eval Tech
• Data Science Safety
• Driving Quality Data Science
• Simulation Data Science
• Pre-Release Data Science
• Expansion Discovery

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!

• Analyze large-scale on-road and simulation data to build metrics, statistical models, and predictive features that quantify the Waymo Driver's safety and driving quality performance
• Develop and enable rigorous evaluation of the Waymo Driver through curating a principled portfolio of tools to assess and inform software development at scale  
• Inform development work on the Waymo Driver by leveraging quantitative analysis to provide early behavioral evaluation of software releases and provide actionable insights for Waymo’s expansion efforts into new territories and hardware platforms 
• Take challenging open-ended research problems and translate them into concrete solutions or prototypes in an empirical setting (e.g., publications, open-source project contribution, internal tools)
• Develop and validate statistical / ML methodology — estimation, uncertainty quantification, sampling, and extrapolation — to use or evaluate large-scale foundation models and draw reliable conclusions about rare and critical events from limited data
• Build tools and pipelines that turn one-off analyses into repeatable workflows, partnering with engineering teams to automate data exploration, triage, and reporting

• Pursuing PhD in a quantitative field (e.g. Statistics, Computer Science, Mathematics, Physics, Engineering, Computational Social Science)
• Proficiency in programming with Python (or R), including data manipulation, wrangling, and visualization
• Strong statistical theory foundation

• Experience in machine learning applications and complex data analysis, or familiarity with SQL and/or C/C++
• Experience in processing and interpreting high dimensional data such as images/videos
• Demonstrated research capabilities through publications and a strong understanding of statistical methodologies and foundation models
• Prior internship experience (Autonomous Driving and generative AI preferred)