Product Quality Engineer, Google Cloud

Minimum qualifications:

• Bachelor's degree in Mechanical Engineering, Industrial Engineering, Materials Engineering, or related engineering discipline, or equivalent practical experience.
• 5 years of experience working in computing hardware quality, product engineering, or electronic hardware systems issue resolution.
• Experience in data analysis and visualization using SQL, JMP, or Python.
• Experience working with reliability and quality test data or manufacturing execution systems.

Preferred qualifications:

• Master's degree or PhD in Electrical, Mechanical, Industrial, Materials, or a relevant engineering discipline.
• Certified Reliability/Quality Engineer (CRE/CQE) certification or equivalent experience.
• 7 years of experience in Hardware Quality, Reliability, Product Engineering, Test Engineering or Manufacturing of cloud hardware systems.
• Experience leading cross-functional engineering teams using a practical and solution-oriented approach.
• Experience with advanced statistical or statistics based methodologies (e.g., design of experiments, statistical process controls, six sigma, etc.).
• Experience in technical leadership, project management, and executive communication.

About the job

As a Product Quality Engineer, you will lead quality initiatives for our AI infrastructure. You will ensure the flawless operation of Google's AI infrastructure and span the entire product life-cycle, from initial concept through volume production and end-of-life, focusing on hardware systems such as advanced AI and computer systems, networking equipment, and data storage solutions. You will collaborate with internal teams to ensure quality is built into every stage of product development. You will also work closely with manufacturing partners to establish robust, repeatable, and scalable processes and quality controls. You will track the field issues and work for early identification and resolution, working with both internal and external partners, and ensure the integration of prior learnings into new product generations.

In this role, you will require an understanding of modern data center hardware infrastructure, including design, manufacturing processes, and testing. You will lead cross-functional teams in a highly collaborative environment. You will extract and analyze data to generate insights and drive decisions.

Behind everything our users see online is the architecture built by the Technical Infrastructure team to keep it running. From developing and maintaining our data centers to building the next generation of Google platforms, we make Google's product portfolio possible. We're proud to be our engineers' engineers and love voiding warranties by taking things apart so we can rebuild them. We keep our networks up and running, ensuring our users have the best and fastest experience possible.

The US base salary range for this full-time position is $111,000-$163,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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

• Develop and execute the overall product quality strategy and assurance plans for next-generation AI hardware systems, collaborating with diverse stakeholders. Develop and manage comprehensive product quality requirements, scope, schedules, and deliverables.
  
• Own the overall product quality process, spanning internal and external development through successful deployment and production at scale, ensuring consistent quality across all stages.
  
• Develop predictive failure models using advanced statistical methods. Track key quality metrics and extract actionable insights using advanced analytics and statistical methods.
  
• Leadthe resolution of product issues during manufacturing and field operations using structured problem solving techniques such as eight disciplines (8D).
  
• Communicate with cross-functional teams and management, distilling complex technical information into clear, concise presentations to facilitate informed decision-making and drive alignment on quality initiatives. Provide regular updates and reports on quality metrics and improvement initiatives.