Waymo is an autonomous driving technology company with the mission to be the 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 One, a fully autonomous ride-hailing service, and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over one million rider-only trips, enabled by its experience autonomously driving tens of millions of miles on public roads and tens of billions in simulation across 13+ U.S. states.

Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you're a software engineer or researcher who's curious and passionate about Level 4 autonomous driving, we'd like to meet you.

Waymo interns work alongside leaders in the industry on projects that deliver significant impact to the company. We believe learning is a two-way street: leveraging 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:

• Evaluating and proposing improvements to the automatic clustering algorithms for reliability issues;
• Building or improving importance sampling techniques to maximize discoveries of novel reliability issues;
• Building or improving real-time monitoring of reliability issues in scaled rider-only deployments, esp. focusing on novel regression monitoring during the ramp-up periods of new release candidates;
• Partially or fully automating the human review processes of reliability issues by extracting rich debugging logs, building productionized data pipelines, and leverage Large Language Models (LLMs) such as Gemini to identify root causes;
• Setting up and validating precursor metrics to measure the capability gaps of the Waymo Simulator, based on existing code coverage statistics.
• The intern will work on scaling the Goldilocks solver. The current approach, which occurs on a single machine, does not scale to large datasets. We would like to replace the single-worker optimization scheme with a distributed optimization scheme on multiple workers. The access required will be the production goldilocks code and the ability to run Flume DSG jobs.

You have:

• Pursuing PhD in a quantitative field (e.g. Statistics, Mathematics, Physics, Engineering, Economics, Political Science)
• Experienced in managing ambiguity, establishing structure in open-ended problem solving, and delivering outcomes (e.g., publication, open source project contribution)
• Familiarity with foundational statistical concepts (hypothesis testing frameworks, Bayesian methods) and practical ML evaluation
• Fluency in Python and C++, basic knowledge of SQL
• Familiarity with PhD-level optimization techniques and literature - namely distributed convex optimization techniques

We prefer:

• 3+ years of experience in data wrangling, visualization and data-driven storytelling
• Fluency in SQL In addition to optimization, it is recommended that the intern also have a background in statistics at the Masters level, namely importance sampling techniques.
• Prior internship in companies (not necessarily in the field of autonomous driving, though that's preferred) In addition to optimization

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.

#LI-Hybrid