Minimum qualifications:
- Bachelor's degree in Engineering or equivalent practical experience.
- 10 years of experience in manufacturing
- Experience in PCBA (Printed Circuit Board Assembly) and related system.
- Experience in design for manufacturability and serviceability.
Preferred qualifications:
- Master's degree or PhD in Electrical, Mechanical, Industrial, Materials, or a related engineering field.
- 12 years of experience in Manufacturing, Quality, or Product Engineering for hardware technology and systems.
- Experience in technical leadership, project management, and executive communication.
- Experience working with ODMs, contract manufacturers and component suppliers for data center server accelerator products (GPU, FPGA or ASIC).
- Experience working with contract manufacturers and suppliers to drive root cause analysis, corrective actions and continuous process improvements
- Knowledge of SQL queries and scripting in Python or Bash.
About the job
As a TPU Product Engineer, you will sit at the absolute epicenter of Google’s AI ambitions, bridging the critical gap between complex machine learning accelerator design and high-volume, high-quality global manufacturing. Next-generation AI hardware is incredibly complex, and our team ensures these systems are manufacturable, testable, and deeply reliable at scale. By joining this team, you will directly enable the rapid expansion and peak performance of Google’s ML services, scaling the physical infrastructure that shapes the future of technology.
You will engage in technical problem-solving at a global operational scale. You will own the manufacturing and test engineering of breakthrough hardware products from the initial concept through the production ramp. You will collaborate early in the life-cycle to influence hardware design for manufacturability, testability, and serviceability (DFx). Once in production, you will architect scalable test infrastructure, analyze real-time build data, and pioneer data-driven optimization strategies to maximize factory yield and eliminate manufacturing fallout. Your work will help optimize capital efficiency, minimize supply chain waste, and support project timelines across the fleet.
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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $171000 - $248000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Own the manufacturing plan and lead reviews between Design and Operations Engineering to drive deliverables that are key to implementing the manufacturing plan, such as DFx and test strategy.
- Provide on-site and remote support for pre-production builds. Ensure factory readiness, support manufacturing line bring-up, provide product debug training and gather feedback on build issues.
- Lead the technology assessment for new products. Work with the product team to influence design decisions, highlight manufacturing risks, and develop mitigation plans.
- Collaborate with Quality and Reliability Engineers to establish NPI and production targets for yield and long-term reliability. Validate product qualification plans, support reliability testing and review results to ensure product performance meets requirements.
- Lead cross-functional teams toward resolution of component/build quality excursions during NPI build phases. Manage bonepile and drive yield bridge analysis to improve product quality.
