Staff Machine Learning Engineer, Inference

Waymo is an autonomous driving technology company with the mission to be the 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 One, a fully autonomous ride-hailing service, and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over one million rider-only trips, enabled by its experience autonomously driving tens of millions of miles on public roads and tens of billions in simulation across 13+ U.S. states.The ML Platform team at Waymo provides a set of tools to support and automate the lifecycle of the machine learning workflow, including feature and experiment management, model development, debugging and evaluation, deployment, and monitoring. These efforts have resulted in making machine learning more accessible to teams at Waymo, ensuring greater degrees of consistency and repeatability. We work with machine learning experts in all parts of the company and our collaborators across Alphabet. You will focus on developing large-scale and efficient inference solutions for Waymo. This is crucial to the development of Waymo Foundation Models for driving including evaluation, auto-labeling and distillation.In this hybrid role, you will report to the Senior Manager of Machine Learning.

You have:B.S. in Computer Science, Math, or equivalent real-world experience2+ years of experience as a technical lead5+ years working with high-scale distributed or ML inference systems for real-time and batch processingUnderstanding of machine learning fundamentals and experience with popular ML frameworks such as JAX, PyTorch, or TensorFlow

We prefer:MS in Computer Science, Math, or equivalent real-world experienceExperience with multi-threaded and stream-based programming modelsPrior experience on ML compiler optimization such as TVM or XLAPrevious experience deploying machine learning models for computer vision, natural language processing, or recommendation systems#LI-Hybrid

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