Principal Engineer, Resource Optimization and Fleet Logic

Responsibilities

• Lead the design and evolution of next generation global fleet planning, defining a multi-year engineering roadmap to deliver 10x more data center capacity with ML, compute & 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 & Infrastructure organizations to define a unified capacity management product suite meeting AI training and inference 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 technology trends.
• - Lead the design and evolution of next generation global fleet planning, defining a multi-year engineering roadmap to deliver 10x more data center capacity with ML, compute & 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 & Infrastructure organizations to define a unified capacity management product suite meeting AI training and inference 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 technology trends.

Minimum qualifications:

Preferred qualifications:

Qualifications

• Minimum qualifications: - Bachelor's degree in Computer Science or 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 field related (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 ability to articulate complex mathematical, economic, and architectural concepts to engineering leaders and executive business stakeholders.