2027 Summer Intern, PhD, Software Engineer, Simulation

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.

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:

• Implement uncertainty aware algorithms that improve the performance of transformer-based multi-task classifiers
• Implement the changes end-to-end, including eval
• Ensure both data and model implementation are correct according to the experiment plan. Pay attention to details about the mechanics of the modeling setup

You have:

• Currently enrolled in a PhD program in Computer Science, Robotics, Electrical Engineering, or a related quantitative field
• Strong programming proficiency in Python and hands-on experience with deep learning frameworks (e.g., TensorFlow, JAX)
• Solid theoretical understanding of machine learning and deep learning fundamentals, including debugging transformer based models with TensorBoard metrics
• Familiarity with software development best practices, including version control

We prefer:

• Uncertainty measurement in deep learning model development
• Hands-on experience using data to improve model performance, as opposed to only focusing on architectural model improvements

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.