Staff Data Center Design Lead
Our thirst for technology is a part of everything we do. The Data Center Engineering team takes the physical design of our data centers into the future. Our lab mirrors a research and development department -- cutting-edge strategies are born, tested and tested again. Along with a team of great minds, you take on complex topics like how we use power or how to run state-of-the-art, environmentally-friendly facilities. You're a visionary who optimizes for efficiencies and never stops seeking improvements -- even small changes that can make a huge impact. You generate ideas, communicate recommendations to senior-level executives and drive implementation alongside facilities technicians.
With your technical expertise, you ensure compliance with codes and standards, develop infrastructure improvements and serve as an expert in your specialty (e.g., cooling, electrical).
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Architect and optimize data centers for large-scale AI/ML deployments, with a deep understanding of GPU/TPU architecture and system integration to maximize performance and efficiency.
- Identify and implement solutions to accelerate project timelines and reduce infrastructure costs while maintaining high performance standards.
- Evaluate emerging technologies and influence industry trends to ensure our data centers are aligned with the latest ML advancements.
- Partner with internal teams and hardware vendors to troubleshoot performance issues, influence product roadmaps, and integrate innovative AI solutions.
Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Power Engineering, a related technical field, or equivalent practical experience.
- 10 years of experience in mission critical facility design and construction environments.
- 5 years of experience in designing and optimizing data centers, with a focus on machine learning systems.
- Experience with GPU/TPU architectures, AI system integration, and performance techniques.
- Experience with data center infrastructure, including power, networking, storage, and cooling systems.
- Experience with cost and performance modeling for data center infrastructure, and ML hardware.
Preferred qualifications:
- Master's degree in Engineering, Business or other relevant fields, or a Professional Engineering (PE) license.