ML Engineer, Foundation Model Infrastructure

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.

- 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

- Masters degree in Computer Science, Machine Learning, Robotics, similar technical field of study, or equivalent practical experience

- 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)

- Strong hands-on SWE skills, able to drive development of large, complex shared codebases

- 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