Senior Staff Software Engineer, Capacity Optimization
Waymo - Mountain View, California, USA
Posted Nov 8, 2025
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
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- Mental health support
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- Relocation assistance
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- Childcare support
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- 401(k) match
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About this role
Senior Staff Software Engineer, Capacity Optimization Mountain View, California, USA 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 Driverâ„¢-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. We are establishing a new team called SCORPIO (SimEval Capacity Operations, Resource Planning, Infrastructure Optimization). This team will be at the forefront of ensuring the efficient and effective use of Waymo's large-scale simulation compute, storage, and network resources. SCORPIO will develop the data-driven models, metrics, and processes to forecast demand, plan capacity, and optimize resource allocation, ultimately improving developer experience and maximizing return on infrastructure investments. You will: As the founding Senior Staff Engineer of the SCORPIO team, you will: - Define the vision, strategy, and technical roadmap for data-driven capacity planning and resource optimization within Waymo's simulation environment. - Lead the development and implementation of sophisticated forecasting models to predict demand for heterogeneous TI resources (CPU, GPU, Storage, Bandwidth, RAM) across various time horizons and simulation workflows. -
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