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 Senior Research Scientist.

You will:

• Build and operate the petabyte-scale data systems and ML pipelines at the heart of Waymo's foundation model development
• Shepherd cutting-edge foundation models from research prototypes to robust components within the Waymo Driver
• Create the automated infrastructure for rigorously benchmarking, continuously monitoring, and safely releasing models
• Wield large-scale compute and frameworks like Flume and JAX to process massive datasets and train/deploy complex models
• Drive significant leaps in the speed, reliability, and efficiency of the end-to-end ML development lifecycle
• Partner with AI Foundations, ML, and Platform experts to transform model innovations into tangible on-road improvements

You have:

• Masters degree in Computer Science, Machine Learning, Robotics, similar technical field of study, or equivalent practical experience
• Proficiency in Python
• Proficiency in C++
• Familiarity with one of the modern deep learning frameworks (e.g. Pytorch, JAX, Tensorflow)
• Experience building or maintaining large-scale data pipelines or ML infrastructure (e.g., Flume, Spark, Borg, Kubeflow)

We prefer:

• Strong hands-on SWE skills, able to drive development of large, complex shared codebases
• Experience in AV planning and related research
• Experience designing and building distributed systems or MLOps platforms (e.g., model versioning, experiment tracking, CI/CD for ML)
• Prior work in an industrial or research setting developing methodologies for the evaluation of ML models

In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:

Health, dental, vision, life, disability insuranceRetirement Benefits: 401(k) with company matchPaid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employmentSick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary)Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeksBaby Bonding Leave: 18 weeksHolidays: 13 paid days per year

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. 

Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.