2027 Summer Intern, MS/PhD, ML Systems & Behavior Discovery

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.

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.

You'll be joining the team that leads the end-to-end integration and evaluation of the Waymo ML Data Flywheel. We work with all stages of the system, from data mining to eval model training to improve the way Waymo develops the Waymo Driver.

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:

• Develop and scale ML pipelines to process telemetry data to discover rare driving interactions

• Benchmark and validate candidate findings to separate meaningful patterns from noise

• Integrate pipelines to turn discovered patterns into concrete, testable artifacts to verify system improvements

You have:

• Currently pursuing graduate degree (Master's or PhD) in Computer Science, Software Engineering or a related technical field

• Strong software engineering skills in Python and/or C++, with experience writing clean, maintainable, and well-tested code

• Demonstrated experience building and operating data or ML pipelines on large-scale datasets (beyond single-machine or notebook prototypes)

• Solid foundational understanding of applied machine learning, statistics, and evaluation methodology (e.g., clustering, anomaly detection, ranking, or metric design)

We prefer:

• Experience working with distributed compute systems, data processing frameworks, or large-scale data lakes (e.g., Apache Spark, Beam/Dataflow, Ray, or internal equivalents)

• Experience with unsupervised learning, outlier/anomaly detection, multimodal embeddings, or LLM/VLM-based evaluation and data filtering

• Track record of taking ambiguous, open-ended problem statements and turning them into practical, working software systems

• Prior research or project experience in autonomous driving, robotics, or spatial-temporal data analysis

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.

Hourly Masters Pay

$70 — $70 USD

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.

Hourly PhD Pay

$85 — $85 USD