Waymo

Waymo

Posted via Greenhouse

2027 Summer Intern, PhD, Research, World Action Model

Posted Oct 3, 2026

Role at a glance

Job function
AI & Data AI Research & Applied Science
Salary
$85/hr
Location
Mountain View, California, United States
Employment
Internship
Education
PhD

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About the role

Original posting provided by Waymo

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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.

The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.

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:

  • Research, implement, and evaluate world action models for end-to-end autonomous driving
  • Collaborate with research and engineering teams to improve the Waymo World Model
  • Conduct cutting-edge research and potentially communicate research findings to the wider academic community via technical reports and/or publication

 

You have:

  • Currently pursuing a Ph.D. in Computer Science, Robotics, Machine Learning, Electrical Engineering, or a related field
  • Strong publication record in top-tier AI/ML or robotics conferences
  • Hands-on experience training and evaluating deep learning models for autonomous driving or robotics applications
  • Strong Python coding skills with expertise in modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow)
  • Deep understanding of modern neural network architectures and learning paradigms

 

We prefer:

  • Experience with diffusion / flow matching models
  • Experience with world action models in autonomous driving or robotics applications
  • Familiarity with autonomous driving datasets
  • Experience with open-source foundation models

 

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 PhD Pay
$85—$85 USD
Waymo

About the company

Waymo

Large Enterprise

Waymo is a leading autonomous driving technology company, originally a part of Google's parent company Alphabet Inc. Established in 2009, Waymo focuses on developing self-driving cars and innovative transportation solutions designed to enhance mobility and safety on the roads. By employing advanced artificial intelligence and machine learning algorithms, the company aims to revolutionize personal and shared transportation, making it more accessible and efficient. Waymo's efforts contribute significantly to the future of autonomous vehicles and the ongoing shift towards smart transportation systems.