About this role

Google is hiring a senior-level Hardware Engineer in the software engineering function based in New Taipei, Taiwan | Zhubei, Taiwan. The posting calls out experience with Python, C, Testing, Machine Learning. Listed education preference: a master's degree or equivalent.

Role: Hardware Engineer
Function: software engineering
Level: senior
Track: Individual contributor
Employment: Full-time
Location: New Taipei, Taiwan | Zhubei, Taiwan
Education: Master's degree
Posted: Jul 7, 2026

AI Summary

Senior Hardware Engineer driving custom silicon from design into high-volume manufacturing. Owns yield optimization, failure analysis, and production screening packages using AI/ML techniques. Requires deep expertise in SoC design, silicon productization, and cross-functional collaboration with factory partners.

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Job description

from Google careers

Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's 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.

We are seeking a highly motivated and experienced Silicon Engineer to drive our Volume Production and Yield efforts.

In this pivotal role, you will be the primary technical owner for the transition of our silicon designs into high-volume factory manufacturing, delivering screening packages to factory partners and driving continuous silicon yield monitoring, failure analysis, voltage margin characterization, and improvement across the product lifecycle. You will leverage AI-driven data analytics and machine learning techniques to optimize yield, accelerate failure debug, and enhance overall silicon quality.

Google's mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of Google AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology.

Responsibilities

Provide technical guidance and expertise for volume productization and post-silicon yield enhancement. Define and deliver silicon screening packages to factory organizations.
Execute and refine the silicon yield management strategy, leading failure analysis, root cause debug of limiters, and production data analysis. Leverage AI/ML tools to automate yield analysis and streamline failure debug.
Engage with cross-functional partners (SiVal, Design, DV, PDTE, Operations, external factory teams) to meet stringent yield goals and resolve critical manufacturing challenges.
Establish processes for screening package releases, data acquisition, and failure analysis protocols. Proactively identify risks and key opportunities for yield across the product lifecycle, from NPI through mass production.
Communicate technical status, risks, and solutions clearly to cross-functional stakeholders and engineering leadership.

Minimum qualifications:

Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
8 years of experience in silicon development, including post-silicon validation, product engineering, test engineering, or yield analysis.
Experience leading a technical project or sub-team.
Experience with embedded systems, firmware, or core software development environments (e.g., C, Python).
Experience with semiconductor manufacturing methodologies, silicon test processes (e.g., ATE, SLT), and fault isolation techniques.

Preferred qualifications:

Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
Experience with delivering technical impact to launch silicon products into high volume manufacturing pipelines.
Experience with the failure analysis and debug of complex Systems on Chip (SoCs).
Experience with a broad range of compute platforms and SoCs (e.g., dedicated mobile or specialized AI processors).
Experience applying AI/ML models, statistical methods, and data analysis for silicon yield optimization.
Excellent problem-solving, people management, and written and verbal communication skills.