Research Scientist

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.

In this hybrid role, you will report to a Principal Scientist.

You will:

• Participate in Waymo’s Foundation World Model post-training and evaluation
• Research and develop cutting edge RL and Distillation techniques for Autonomous Vehicle Trajectory Planning
• Integrate emerging research from the broader AI community into Waymo’s internal RL infrastructure, conducting rigorous ablations to identify and scale the most promising methods
• Partner with engineering and research teams across Waymo to share recipes, techniques, and post-training best practices to accelerate our collective know-how

You have:

• PhD or Masters in Computer Science, Machine Learning, Robotics, or a similar technical field; with 3+ years of industry or post-doc research experience in Reinforcement Learning or Foundation Models
• Demonstration of original contributions to the field through high-impact publications (ArXiv, peer-reviewed conferences like NeurIPS/ICLR/CVPR), technical blog posts, or significant open-source contributions
• Proficiency in implementing model training flows in a scalable, distributed and performant manner such as Data parallel, FSDP and other sharding approaches
• A willingness to work with complexity of globally distributed inference infrastructure

We prefer:

• PhD in Computer Science, Machine Learning, or Robotics, with a research focus on Reinforcement Learning, Foundation Models, or Multi-Modal learning