Hardware Systems Quality and Reliability Engineer, Platforms

Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Engineering, Hardware Engineering or equivalent practical experience.
• 4 years of experience across data center system hardware domains, such as semiconductors, peripheral component interconnect express (PCIe), power electronics, central processing unit/xprocessing unit (CPU/xPU) architectures, networking, embedded systems, and servers.
• Experience in data curation, mining/analysis, visualization, and scripting utilizing tools such as SQL, JMP, Python, R, Tableau, or similar.

Preferred qualifications:

• PhD or Master's degree in Electrical Engineering, Computer Engineering or Hardware Engineering.
• Experience in technical project management and effective communication with executive stakeholders.
• Experience in quality and reliability engineering roles.
• Experience leading multidisciplinary engineering teams through technical issues.
• Proficiency in statistical methodologies, predictive modeling, and data visualization techniques.
• Familiarity with fault isolation (FI) and other failure analysis (FA) methodologies.

About the job:

Our team identifies and resolves fleet-wide technical issues, implementing product and methodological enhancements to maximize hardware system reliability while ensuring efficient deployment and maintenance within data center environments. We conduct analysis of fleet data to address systemic issues and implement preventative measures to ensure long-term stability.

In this role, you will require physical interaction with hardware equipment in a simulated data center environment, utilizing Google labs, power, and safety equipment. Regular development and processing of engineering hardware must be performed on-site.

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.

The US base salary range for this full-time position is $132,000-$189,000 + 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:

• Collaborate on data center hardware platforms across a wide range of domains, including semiconductors, test, peripheral component interconnect express, power, central processing unit, x processing unit, power electronics, and networking.
• Provide technical leadership by establishing priorities, conducting comprehensive root cause analyses, and resolving technical issues to ensure fleet quality and a stable customer experience.
• Optimize system health and repairability by improving mean time between failures, managing swap rates, and developing advanced repair strategies.
• Partner with system software and diagnostics/test teams to enhance the detection, characterization, and resolution of fleet-scale hardware failures.
• Lead the initiation and implementation of innovative product, process, and tool enhancement projects within cross-functional environments and integrate lessons learned from field performance data into new product introduction.