Silicon Validation Product Engineer, Google Cloud
Confirmed on the employer's own hiring board on Nov 3, 2026. First seen by Alion on Nov 2, 2026. Google scores B on the Alion truth index.
About the job
In this role, you’ll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You’ll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.
In this role, you will support integration and testing on hardware emulation platforms. During post-silicon, you will lead validation, bringup, and system qualification across power features, interfaces, HBM memory, compute functionality, and overall performance. Finally, you will provide ongoing engineering support during chip manufacturing, deployment, and field operations-monitoring quality and reliability while optimizing test efficiency, RMA/FA root-cause analysis, and process workflows.
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: $138000 - $197000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Own sustaining phases of the silicon development lifecycle. Monitor, debug, and deploy corrective actions for quality and reliability issues in manufacturing, deployment, and serving.
- Optimize test performance across manufacturing and deployment. Partner with cross-functional teams to improve test coverage and reduce overall test time.
- Drive triage, debugging, and corrective actions across all deployment stages. Support rapid resolution in early NPI to reduce test failures and escapes, driving process improvements as necessary.
- Collaborate with supply chain, contract manufacturing, and vendors to implement firmware, software, hardware, or process improvements.
- Investigate, reproduce, and root-cause field failures to drive resolution, focusing primarily on functional operations alongside DFT/DFD structural testing.
Qualifications
Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
- 4 years of experience with silicon or semiconductors (e.g., digital design, silicon validation, computer architecture, product engineering).
- Experience scripting in one or more programming languages (e.g., Python).
Preferred qualifications:
- Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
- Experience in product engineering: managing and optimizing test content, debugging issues, performing RMA/FA, and conducting root cause analysis.
- Experience collaborating with silicon vendors, contract manufacturers, and supply chain partners to improve test quality, reliability, and yield.
- Experience with large digital SoCs, focusing on functional domain engineering, alongside utilizing complex software stacks to perform functional testing, investigations, and log/data analysis.
- Experience with high-speed interfaces, ASIC interfaces, memory systems (HBM), power system features, or high-power ASICs.