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Salary
≈ $141k – $258k per year (Estimated)
Location
In office (Sunnyvale)
Seniority
Senior · 4+ years exp

Confirmed on the employer's own hiring board on Oct 15, 2026. First seen by Alion on Oct 15, 2026. Google scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Google is an American technology company founded in 1998 by Larry Page and Sergey Brin and now the principal subsidiary of Alphabet, headquartered in Mountain View, California. It operates the world's dominant search engine and the advertising system built around it, along with YouTube, Android, Chrome, Gmail, Maps, Workspace and Google Cloud, reaching billions of users across nearly every internet-connected market. The company designs its own silicon in the Tensor Processing Unit line, develops the Gemini foundation models through Google DeepMind, and derives most of its revenue from advertising while cloud has become its fastest growing segment.

About the job

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.Our Platforms Infrastructure Engineering team designs and builds the

hardware and software technologies that power all of Google's services. Our computational challenges are complex and unique, enabled by cutting-edge custom hardware designed and made in-house. As a hardware engineer, you will design and build the systems that are the heart of the world's largest and most powerful computing infrastructure. You will see those systems from concept all the way through to high-volume manufacturing. Your work has the potential to shape the machinery that goes into our cutting-edge data centers, affecting millions of Google users.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $159000 - $230000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

  • Create test plans and coordinate resources across test environments.
  • Perform electrical, functional, performance and reliability testing for Google's machine learning trays and solutions, and analyze results to ensure that they meet Google's requirements.
  • Report bugs and drive corrective action where needed with external suppliers and internal Supplier Quality Engineers (SQEs), commodity teams and developers.
  • Use scopes, protocol analyzers, Bit Error Rate Testers (BERTs) and other lab equipment to collect high precision data.
  • Organize results, communicate findings and incorporate learnings to maintain and improve the qualification process.

Qualifications

Minimum qualifications:

  • Bachelor’s degree in Electrical Engineering, Computer Engineering, Physics, a related field, or equivalent practical experience.
  • 4 years of experience working in a Hardware Qualification technical environment, or 3 years of experience with an advanced degree.
  • Experience with testing requirements and algorithms for data center hardware or systems, and using scripting and automation to execute test algorithms and analyze results.
  • Experience with lab equipment (e.g., scopes, analyzers) to collect traces.

Preferred qualifications:

  • Master's or PhD degree in Electrical Engineering, Computer Engineering, Physics, or a related field.
  • 1 year of experience in technical leadership.
  • Experience with scripting and automation to execute test algorithms, and then analyze results. Familiarity with lab equipment like scopes, BERTs and analyzers.
  • Experience determining the appropriate data to collect to facilitate quick, accurate failure analysis from vendors.
  • Experience with ML hardware components, memory systems, and interconnect technologies. Understanding of ML systems and how to test the interfaces.
  • Understanding of signal integrity, high speed interfaces, and best practices to collect accurate Signal Integrity (SI) data.
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