Principal Engineer, Resource Optimization and Fleet Logic

The application window will be open until at least September 29, 2026. This opportunity will remain online based on business needs which may be before or after the specified date. By applying you may share your preferred working location from the following: Thornton, CO; Atlanta, GA; Austin, TX; Sunnyvale, CA (USA).

Minimum qualifications

• Bachelor’s degree in Computer Science or a similar technical field, or equivalent practical experience.
• 15 years of experience as a software engineer.
• Experience delivering large‑scale capacity planning, IaaS/PaaS solutions, or fleet management systems.

Preferred qualifications

• Master’s degree or PhD in Computer Science or a related field (e.g., Networking or Security Systems).
• Experience architecting, leading, and delivering large‑scale capacity planning, combinatorial optimization, fleet management, or distributed infrastructure transformations from concept to deployment.
• Deep understanding of modern AI/ML infrastructure demands (TPU/GPU topologies, accelerators) with the ability to integrate AI‑driven solutions.
• Ability to influence and lead without direct authority, building strong cross‑organizational relationships across disparate teams (e.g., Hardware, Software, Supply Chain, and Product Management).
• Exceptional communication skills and the ability to articulate complex mathematical, economic, and architectural concepts to engineering leaders and executive business stakeholders.

About the role

Google runs one of the largest computational fleets in the world, with compute, storage, networking and dedicated accelerators spread across all continents. As a Principal Engineer, you will own the architectural outlook and end‑to‑end technical strategy for the global capacity planning ecosystem, serving as the primary technical authority for complex planning constraints, optimization scenarios, and machine deployment workflows. You will pioneer the technological vision for Google’s planetary‑scale capacity planning and fleet optimization architecture, defining a multi‑year path to orchestrate data‑center capacity planning, placement, supply planning, and fleet reconfiguration for ML, compute, and storage demand, ultimately empowering the company’s largest internal and external AI/ML customers.

Responsibilities

• Lead the design and evolution of next‑generation global fleet planning, defining a multi‑year engineering roadmap to deliver 10× more data‑center capacity with ML, compute, and storage resources.
• Leverage deep technical knowledge across the data‑center stack—spanning ML hardware (TPUs/GPUs), general compute, power, cooling, physical space, supply‑chain workflows, and network topology—to model, forecast, and dynamically reconfigure fleet resources.
• Partner across AI and Infrastructure organizations to define a unified capacity‑management product suite that meets AI training and inference needs for internal and external customers.
• Integrate cutting‑edge AI/ML, operations research, and advanced mathematical optimization into capacity‑planning workflows to compress capacity cycles, eliminate stranded capacity, and maximize data‑center utilization.
• Collaborate with academic institutions and industry pioneers to anticipate emerging technology trends.

Pay

US: $307,000 – $427,000 (USD) + 30 % bonus target + equity + benefits.