Staff Machine Learning Engineer, Data Flywheel

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 DriverTM—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 Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the autonomously driving vehicle (ADV), i.e., the system that “perceives” the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently and continuously learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world, and (4) optimize models for our onboard and offboard hardware.

In this hybrid role you will report to a Technical Lead Manager.

You will:

• Create large scale data sets and training recipes, develop methods and recipes for human and machine labeling of data sets
• Develop methods for data mining and recipes for automated data collection/model update flywheels
• Develop methods and recipes for evaluating real-world performance of models, and detecting regressions in model updates
• Understand the data needs of the problem domain team and design scalable infra solutions that support model improvement and product expansion.
• Design, build and implement ML data infra and validate the changes to support the continuing scaling of VLM data needs.
• Collaborate with ML infrastructure teams and the problem domain team to address issues and bottlenecks and streamline validation.