PhD ML Summer Intern

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since starting as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver, which aims to improve access to mobility and save thousands of lives lost to traffic crashes each year. 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. It has provided over ten million rider-only trips, based on more than 100 million miles driven autonomously on public roads and tens of billions of miles in simulation across 15+ U.S. states.

Software Engineering builds the intelligence behind Waymo's fully autonomous driving technology. Our software lets the Waymo Driver perceive its surroundings, make the right decision in every situation, and get people safely to their destinations. Our teams tackle complex technical problems in robotics, perception, decision-making and deep learning, working closely with hardware and systems engineers. If you are a software engineer or researcher who is curious about and passionate about Level 4 autonomous driving, we would like to hear from you.

Waymo interns work with industry leaders on projects that make a real impact. We see learning as a two-way exchange, where you apply your knowledge while gaining chances to grow your skill set. Interns are an important part of our culture and our recruiting pipeline.

What you will do

• Design, implement and scale Reinforcement Learning post-training and distillation algorithms (such as flow or diffusion policy optimization, off-policy importance sampling and constrained multi-objective RL) for foundation world models in simulation and autonomous driving.
• Develop learned reward models, realism critics and discriminators within a closed-loop multi-agent simulator, aligning simulated agent and driving behavior with human driving realism and safety.
• Run rigorous closed-loop ablation studies on large-scale TPU clusters, using agentic auto-research workflows and fleet-scale driving behavior metrics (such as route progress, braking comfort, lateral stability and lane selection) to reduce simulator exploitation and distribution shift.
• Write clean, modular and well-tested JAX code in core simulation and autonomous driving codebases, working closely with researchers and engineers across simulation, world modeling and autonomous driving teams.

This is a hybrid onsite internship. Resumes are accepted on a rolling basis until the role is filled. To be considered for multiple roles, you must apply to each one individually, so please apply to the top 3 roles you are interested in.