Bachelor's degree in Computer Science, Electrical Engineering, or a related technical field, or equivalent practical experience.
5 years of experience in designing and optimizing data centers, with a focus on machine learning systems.
Experience with cost and performance modeling for data center infrastructure, and ML hardware.
Experience with GPU/TPU architectures, AI system integration, and performance techniques.
Experience with data center infrastructure, including power, networking, storage, and cooling systems.
Preferred qualifications:
Master's degree in Computer Science, Electrical Engineering, or a related field.
About the job
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).
The Data Center team designs the foundational blueprints for Google's future data center infrastructure.
In this role, you will be responsible for defining and developing system-level architectures for new Data Center concepts and designs, creating and maintaining the long-term technical roadmap for Data Center evolution, emphasizing scalability, efficiency, and speed, driving innovation in power distribution, cooling solutions, and layouts to support increasing power densities and new technologies. You will lead the physical infrastructure integration of critical hardware programs, including Machine Learning platforms Tensor Processing Unit/Graphics Processing Unit (TPUs/GPUs) and managing system interfaces and ensuring seamless compatibility between various data center products and subsystems. You will be collaborating across numerous partner teams to translate product and service requirements into buildable, operable, and maintainable data center designs.
The US base salary range for this full-time position is $144,000-$211,000 + 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 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 AI solutions.
Google
Google LLC started as a PhD project by Larry Page and Sergey Brin in 1998 at Stanford University. Google LLC has blossomed into a behemoth of the tech world. With its mission to organize the world's information and make it universally accessible and useful, Google’s search engine is its crown jewel. Online advertising, via AdWords and AdSense, forms the backbone of its financial success. Beyond search, Google has ventured into cloud computing, hardware, and software development. The innovative PageRank algorithm revolutionized search engine technology, and surviving the dot-com bubble burst and going public in 2004 spurred its meteoric growth. Acquiring YouTube stands as a testament to Google’s strategic expansion.