Product Quality Engineer, AI/ML, Hardware, Google Cloud

Product Quality Engineer, AI/ML, Hardware, Google Cloud

Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.

Google Cloud is powered by advanced compute, network, storage, and Artificial Intelligence (AI) platforms, built on one of the world's largest and most sophisticated Technical Infrastructures (TI). The Cloud Supply Chain and Operations (CSCO) teams are responsible for the fast and efficient deployment of this infrastructure. The Global Hardware Quality and Reliability (GHQR) team ensures predictable quality and reliability across all hardware components, systems including Tensor Processing Unit/Graphics Processing Unit (TPU/GPU) AI platforms and data center infrastructure. This hardware is the backbone of Google Cloud and its AI/ML capabilities, directly contributing to Google's engaged edge. As a Product Quality Engineer, you will own the quality and reliability strategy for Google's TPU/GPU-based AI/ML platforms. You will be the quality expert, collaborating with cross-functional partners in Design, Manufacturing, and Operations to embed quality into every product. You will also analyze data, drive root cause analysis, and influence process improvements.

The US base salary range for this full-time position is $165,000-$245,000 + 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.

Define and own the quality and reliability strategy for TPU/GPU hardware across its entire life-cycle, from design through field support.

Lead the resolution of systemic quality issues in manufacturing and the field, driving Root Cause and Corrective Actions (RCCA) using structured methodologies.

Collaborate with engineering teams to influence design specifications, qualification plans, and test coverage to ensure product robustness and mitigate early risks.

Establish and monitor key quality KPIs (e.g., Average Severity Rate (ASR), Average Failure Rate (AFR), etc.). Analyze manufacturing and field data to develop predictive models and drive continuous improvement in design and processes.

Act as the primary point for customer quality, managing escalations and integrating feedback. Oversee quality and corrective actions with suppliers, including Return Material Authorizations (RMA) and Process Change Notifications (PCN) qualification.