Staff Software Engineer, 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 Driver™—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 SCORPIO (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. You will: As the founding Lead of the SCORPIO 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. Outcome Analysis: Build systems to translate resource plans into tangible outcomes (e.g., queue lengths, user demand fulfillment) and develop attribution models for capacity imbalances. Cross-Functional Collaboration: Partner with Simulation, Infrastructure, and Finance teams to translate business requirements into technical specifications and automated solutions. Technical Leadership: Provide technical guidance on the intersection of quantitative modeling and systems engineering, mentoring junior members and influencing the technical roadmap for SCORPIO. You have: Bachelor's degree in Computer Science, Mathematics, Statistics, Operations Research, or a related quantitative field, or equivalent practical experience. 8+ years of experience in software engineering, with a strong focus on distributed systems, large-scale data processing, or quantitative engineering. Proficiency in C++ or Python, with experience building and deploying production-level software. Experience with large-scale distributed systems and cloud infrastructure (e.g., GCP, AWS, Azure). Strong background in quantitative methods, such as optimization, statistical modeling, or time-series analysis. Expertise in SQL and working with large-scale data warehouses (e.g., BigQuery). We prefer: PhD or Master's degree in a quantitative field or Computer Science. Experience in Capacity Engineering, Infrastructure Optimization, or Site Reliability Engineering at scale. Familiarity with ML-driven forecasting and optimization techniques. Experience with financial modeling or cost-benefit analysis of tech