Google's Cloud infrastructure is one of the world's most advanced compute, network, and Artificial Intelligence (AI) platforms, providing customers with speed, efficiency, intelligence, and high reliability. The Cloud Supply Chain and Operations (CSCO) team ensures this technical infrastructure is delivered with speed, reliability, and efficiency.
Quality and reliability are core values, crucial to deliver our customers with exceptional products and services. The Global Hardware Quality and Reliability (GHQR) team is responsible for accelerating end-to-end product introduction and delivering predictable product quality and reliability, directly contributing to Google's competitive advantage.
In this role, you will lead quality initiatives for the Graphics Processing Unit (GPU) platform Artificial Intelligence (AI) infrastructure. You’ll ensure the flawless operation of hardware systems including Graphics Processing Unit (GPU) servers, networking gear, and data storage from initial concept through End-of-Life (EOL).
You will collaborate with internal teams and manufacturing partners to build quality into every stage of product development, establish processes, and track and resolve field issues. You’ll bring a deep understanding of modern data center hardware, including design, manufacturing, and testing. As a proactive problem-solver with a sense of ownership, you will contribute to and lead cross-functional teams in a changing environment. This role may require traveling both within the United States and internationally up to 25% of the time.
The US base salary range for this full-time position is $120,000-$172,000 + 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
- Own the product quality process, managing suppliers and Joint Development Manufacture (JDM) partners from initial development through data center deployment to ensure consistent reliability.
- Develop predictive failure models and track key quality metrics using advanced statistical methods to extract actionable insights throughout the entire product life-cycle.
- Lead product issue resolution during manufacturing and field operations, utilizing structured problem-solving techniques such as the 8 Disciplines (8D) methodology.
- Drive alignment on quality initiatives by distilling technical data into concise presentations for cross-functional teams and management to facilitate informed decision-making.
- Execute the quality strategy and assurance plans for next-generation Graphics Processing Unit (GPU)-based AI hardware systems, managing comprehensive requirements, schedules, and deliverables with key stakeholders.
Qualifications
Minimum qualifications:
- Bachelor's degree in Mechanical Engineering, Industrial Systems Engineering, or Manufacturing, or equivalent practical experience.
- 5 years of experience in quality, manufacturing, or product engineering of cloud hardware technology or electronic hardware systems.
- 3 years of experience in continuous improvement methods in manufacturing including 6 sigma/lean methodologies and managing change at suppliers and contract manufacturers.
- 2 years of experience working on PCB assembly, design validation, component qualification, data analysis, and scripting.
Preferred qualifications:
- Master's degree or PhD in Electrical, Mechanical, Industrial, Materials or other relevant engineering field.
- Experience in Quality/Reliability/Production operation, with substantial direct experience in New Product development and qualification.
- Experience leading cross-functional teams for continuous quality issue investigation and quality improvement using practical approaches (e.g., Six Sigma, Lean Manufacturing, Quality Metrics, DOE, SPC) and experience in technical leadership.
- Experience with analytics (e.g., JMP/Minitab) and programming (e.g., SQL/Python) to identify and resolve root causes.
- Experience with manufacturing processes (e.g., PCBA, mechanical assembly, integration and test/debug methods).