Senior Data Scientist, Simulation Capacity Optimization

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.

Our Simulation team is at the heart of this mission, enabling us to safely and rapidly iterate on the Waymo Driver.

We run billions of miles of simulations, creating a massive and complex demand for technical infrastructure resources (CPU, GPU, TPU, Storage).

We are establishing a new team called CORPIO (SimEval Capacity Operations, Resource Planning, Infrastructure Optimization).

This team is tasked with building a critical capability for Waymo: data-driven, strategic capacity planning and resource optimization.

We are looking for a Quant Software Engineer at the L6 level to bridge the gap between sophisticated mathematical modeling and production-scale infrastructure automation.

You will be responsible for building the technical systems that forecast demand, optimize resource allocation, and automate infrastructure management, ensuring our simulation environment is both high-performance and cost-effective.

As a Senior Data Scientist on the CORPIO team, you will:

• Infrastructure Modeling & Automation: Design and build production-grade systems and pipelines to automate capacity planning, demand management, and quota allocation.
• Quantitative Forecasting: Implement and maintain sophisticated models for infrastructure demand forecasting, incorporating architectural shifts, peak loads, and time-shifting opportunities.
• Resource Optimization Algorithms: Develop and deploy algorithms to optimize resource utilization across a heterogeneous fleet (CPU, GPU, TPU) and diverse supply models (on-demand vs. reserved).
• Data Pipeline Engineering: Architect and maintain robust data pipelines that ingest infrastructure telemetry and demand driver signals to feed forecasting and optimization engines.