Senior Software Engineer, ML Evaluation Infra and Efficiency

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 Waymo ML Frameworks & Efficiency team partners closely with Research and Production teams across Waymo to develop and deploy models in Perception and Planning that are core to our autonomous driving software.

We help our partners by offering the best frameworks for the entire model development lifecycle, including training and evaluation.

They are geared towards both scaling models with efficiency and solving problems unique to ML for autonomous driving.

We are looking for engineers with ML system expertise to help us train, evaluate and improve pre-trained models to be deployed into Waymo Driver, and potential future products.

You’ll work closely with researchers and modeling engineers across the company, and tackle the challenges of different evaluation scenarios for Waymo drivers, building large-scale evaluation platforms that can scale across compute, data, and environments to improve model intelligence and alignment with human drivers.

Design and build distributed evaluation platforms for large-scale ML evaluation workloads.

Profile evaluation platforms, identify performance bottlenecks (CPU, memory, I/O, network), and implement optimizations to improve inference speed and resource utilization.

Collaborate with ML engineers to understand evaluation requirements and scenarios, and improve DevX of the evaluation infrastructure.

Improve runtime goodput of ML inference workload and efficiency of metrics computations, ensuring scalability and reliability across distributed environments.