About the job
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 Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver’s behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently.
Responsibilities
Design, implement, and evaluate state-of-the-art generative models for autonomous vehicle planning and prediction
Develop next-generation, ML-powered systems that enhance the capabilities of the ML driver and accelerate the rapid scaling of Waymo’s business
Translate open-ended, real-world driving challenges into well-defined machine learning problems, applying cutting-edge techniques, including foundation models and reinforcement learning
Write high-quality, scalable, and thoroughly tested code to bring cutting-edge research into production
Partner with world-class researchers, engineers, and product managers to deliver safe and smooth planning behaviors, and publish findings at top-tier academic venues
Qualifications
Minimum
PhD in Computer Science, Machine Learning, Robotics, a related technical field, or equivalent practical experience
A proven track record of publications in top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICRA, IROS, RSS, CoRL, ACL, or EMNLP)
Demonstrated impact on the broader ML community through influential research, widely adopted open-source projects, or significant industry contributions
Hands-on expertise with modern deep learning frameworks, for example JAX or PyTorch
Proficient programming skills in Python and/or C++, coupled with strong analytical and debugging abilities
Preferred
Specialized research experience in deep learning, reinforcement learning, causal reasoning, or foundation models
Prior industry experience (e.g. internships) in applied ML research or software development
Domain expertise in solving motion planning, prediction, or related robotics problems
Hands-on experience deploying, evaluating, and maintaining ML-based systems in real-world, production environments