Principal Engineer, Data Center Systems Architecture
- Location
- Atlanta, GA, USA
- Work model
- On-Site
- Level
- Principal
- Salary
- $256k – $356k/yr
- H-1B history
- 2,460 approvals (FY2023)
- Posted
- 2h ago
Skills
About this role
Google Cloud provides organizations with leading infrastructure, platform capabilities, and industry solutions. We deliver enterprise-grade cloud solutions that leverage Google’s technology to help companies operate more efficiently and adapt to changing needs, giving customers a reliable foundation for the future. Customers in more than 150 countries turn to Google Cloud as their trusted partner to solve their most critical business problems. Google's custom-designed machines and data centers constitute one of the largest, most efficient, and most powerful computing infrastructures in the world. As a Principal Engineer for Systems Architecture within Google’s Data Center organization, you will define and drive next-generation architectures at the critical intersection of physical data center infrastructure and IT systems. In this role, you will partner with Hardware, Software, Manufacturing, Supply Chain, Delivery, and Operations teams across Google to lead game-changing architectural and development initiatives. You will work closely with internal engineering teams and external industry partners to execute these architectures, providing the technical direction, operational guidance, and industry influence necessary to scale Google's infrastructure for the future. The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide. We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $256000 - $356000 (USD) + 30% bonus target + equity + benefits Learn more about benefits at Google .
Provide direction and leadership for data center and equipment designs, cross-system optimizations, and global operational strategies. Lead cross-disciplinary architecture, definition, and development across engineering fields (e.g., electrical, mechanical, thermal, structural, controls, industrial) to optimize system design points. Drive modular data center design initiatives, pioneering scalable, prefabricated, and containerized architectures that reduce deployment timelines and enhance reproducibility. Deliver technical direction, integration expertise, and data analysis for overall system architecture, throughput, and performance, while presenting option tradeoffs to guide executive decision-making. Collaborate across teams and partners to design for manufacturability and supply chain efficiency, accelerating delivery speed while optimizing total cost of ownership. Analyze operational flows and manufacturability to maximize speed-to-market and capacity serving. Drive standardization efforts to achieve global scale, supply chain resilience, and optimized success criteria to include reliability, cost, weight, transportation, installation, and serviceability.
Minimum qualifications: Bachelor’s degree in Electrical Engineering, Mechanical Engineering, Computer Science, a related technical field, or equivalent practical experience. 15 years of experience in data center physical infrastructure architecture, systems engineering, and facility design. Experience aligning data center physical infrastructure with hardware architectures optimized for Machine Learning (ML) and Artificial