Staff Tech Lead, Machine Learning Engineer, Perception

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 self-driving car, 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'll report to a Staff Technical Lead Manager.

You will:

• Lead the design and development of multi-sensor, multi-task model architectures for dense and complete scene understanding with a focus on key tasks including object detection, occupancy prediction, road understanding, and trajectory planning.
• Architect and develop the foundational modeling libraries and software powering model development and experimentation.
• Design and implement scalable core model components, including sensor encoders, sensor fusion modules, backbones, and temporal aggregation / memory blocks.
• Adapt SOTA ML technologies to unique compute requirements, leading optimizations for latency, throughput, and memory to operate efficiently within strict compute budgets.
• Drive rapid prototyping by building and maintaining flexible pipelines and development environments for fast iteration on novel model designs, enabling seamless integration of field advancements and in-depth analysis of model training dynamics and learned representations.