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Required Signal/Power Integrity Engineer, PhD Graduate
About the job
Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our 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.
The ML, Systems, & Cloud AI (MSCA) organization designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all our services (Search, YouTube, etc.) and Cloud.
We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Clouds Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.
Responsibilities
Simulate high speed interface electrical behavior using HSPICE or other circuit simulators.
Generate precise electrical models (e.g., S-parameters, SPICE models) for components such as packages, PCBs, and connectors for use in simulations.
Collaborate extensively with cross-functional teams, including ASIC architects, digital/analog designers, physical design/layout engineers, and system engineers, to define specifications and resolve integration challenges.
Execute lab measurements utilizing test equipment like oscilloscopes, Vector Network Analyzers (VNA), Time Domain Reflectometers (TDR) , Spectrum analyzers to validate simulation outcomes and debug signal and power-related issues on silicon prototypes and boards.
Establish robust design rules and guidelines for optimal signal/power integrity during PCB and package layout, ensuring high production yield and reliability.

Required SoC Quality and Reliability Engineer, Cloud
About the job
In this role, youll 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 our most demanding AI/ML applications. Youll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of our 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.
Our data centers are the most advanced in the world. In this role, you will help build the state-of-the art SoCs that power these data centers by driving quality and reliability processes from the Integrated Circuit perspective. This is an opportunity to create silicon and follow it into the field (and back!) to drive improvements for the next-generations of chips.
In this role, you will understand Integrated circuit (IC) flows, wafer processing, testing, qualification, yield, reliability, and failure analysis. You will work with various cross-functional teams to develop quality and reliability specifications, develop and deploy design guidelines, and develop and execute and test plans. Within the larger organization you will collaborate with global hardware quality and reliability teams, silicon design, validation and engineering teams.The AI and Infrastructure team is redefining whats possible. We empower customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity.
Responsibilities
Drive the strategic definition and development of Design-for-Reliability (DfR) guidelines, collaborating with cross-functional subject matter experts to integrate reliability into early design stages.
Define and lead the development of qualification hardware and test methodologies, managing internal teams and external vendors to ensure robust silicon and package verification.
Execute comprehensive silicon and package qualification programs (including HTOL, ELFR, ESD/LU, b/HAST, etc.) and conduct in-depth failure analysis to resolve quality issues.
Extract and analyze large-scale data from qualification programs, high-volume manufacturing, and field returns to identify failure mechanisms and trends for yield and reliability optimization.
Develop and implement physics-based statistical Quality and Reliability models (e.g., ELF, TDDB, NBTI) to predict device failure mechanisms and lifetime behaviors.