2027 Summer Intern, BS, Software Engineer, Model Eval

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.

The subteam for this role will be:

The Model Eval team has a lot of projects that evolve quickly. We will help the Intern find a project that matches their skill set during the internship. Possible projects include:

Eval pipeline optimization (orchestration level): Finding opportunities to decrease e2e execution cost by reducing overhead or increasing parallelism in jobs

Eval pipeline optimization (model level): Improving eval workload efficiency by improving exported model efficiency in CPU or TPU export configurations

Pipeline observability: Providing insights and high level metrics on pipeline runtime and success rate

LLM integration: Finding better ways to integrate agents with eval workflows. This could be making it easier for agents to run evals accountably, or making it easier for agents to contribute maintainable code to the eval codebase

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:

Reading/writing pipeline and implementation code

Profiling and understanding slowness within distributed systems

Building custom harnesses to isolate components of the system

Querying existing logs and data sources for aggregate statistics

You have:

Currently pursuing a Bachelor’s degree in software engineering or a related field

Working knowledge of Python

Understanding of ML fundamentals of training and evaluating models

The ability to use agents for learning and implementation, and the wisdom to know when each is appropriate

We prefer:

Distributed systems analysis or optimization

Model performance techniques like sharding, quantization, and batching

Working knowledge of C++

General Perks

Help solve challenging problems with a direct impact on the company

Competitive compensation packages with a housing/relocation bonus (if applicable)

Medical, dental, and vision insurance

Fun intern events and networking opportunities

Onsite Perks

Free breakfast, lunch, dinner, and snacks

Free access to Google shuttles

Onsite gym

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 Bachelors Pay

$60—$60 USD