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 Computer Science, Electrical Engineering, Mechanical Engineering or a related field.
- Experience in large campus-scale data center design concepts.
- Experience with current and emerging trends in ML hardware and software, and their impact on data center design.
About the jobWith 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).
The US base salary range for this full-time position is $171,000-$248,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
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 an 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.
