About the role#
This is a full-time, paid internship for the Summer 2027 term. You will work with the Product Data Science team to support the development and scaling of the Waymo Driver. This role is based in San Francisco and follows a hybrid work model.
What you'll do#
- Refine causal and predictive pricing models by adding feature granularity and proper regularization.
- Enhance pricing models through improved time series estimation.
- Quantify the loss that results from pricing model mispredictions.
What you'll need#
- Current enrollment in a PhD program.
- Expertise in causal inference or econometrics.
- Experience with predictive modeling.
- Proficiency in Python and SQL.
- Preference for candidates with experience in Causal Machine Learning, such as Double Machine Learning.
- Preference for candidates with previous experience at ride-hailing or marketplace companies.
Location & details#
- Location: San Francisco, California.
- Term: Summer 2027.
- Pay: $85 per hour.
- Work modality: Hybrid.
- Majors: Data Science, Computer Science, Statistics, Mathematics, or Economics.
About Waymo
Waymo is a technology company that develops automated driving systems. It focuses on mobility and road safety through its Waymo Driver technology. The firm operates as a private organization and employs between 1,001 and 5,000 people. It was founded in 2009 and maintains its headquarters in Mountain View, California.
How to get in at Waymo
Applying early is a significant advantage for roles at Waymo, as recruiters often review candidates before the applicant pool grows too large. Intern Insider sends an instant alert the moment a role matching your target is published anywhere, so you can submit your materials among the first. This helps ensure your application is seen before the pile grows. You can also improve your response rates by reaching out to the people actually hiring for these positions. Intern Insider surfaces the recruiters behind the company roles, allowing you to contact them directly to ask about the team or a potential referral. It is a more effective approach than sending your resume into a queue.



