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Salary
≈ $28k – $64k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Staff · 10+ years exp

Confirmed on the employer's own hiring board on Nov 1, 2026. First seen by Alion on Oct 27, 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.

In this role, you will 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 will be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You will 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.

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.

Responsibilities

  • Drive development of complex IPs and subsystems along with a team of engineers.
  • Own micro-architecture and implementation of IPs and subsystems.
  • Work with architecture, firmware and software teams to drive feature closure and develop micro-architecture specifications.
  • Drive design methodology, libraries, debug, code review in coordination with other IPs Design Verification (DV) teams and physical design teams.
  • Identify and drive power, performance and area improvements for the domains owned.

Qualifications

Minimum qualifications:

  • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
  • 10 years of experience in ASIC development with Verilog/SystemVerilog, and Vhsic Hardware Description Language (VHDL).
  • Experience in micro-architecture and design of IPs and Subsystems.

  • Experience in ASIC design verification, synthesis, timing/power analysis, and Design for Testing (DFT).

Preferred qualifications:

  • Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
  • Experience with scripting languages (e.g., Python or Perl).
  • Experience in SoC designs and integration flows.
  • Knowledge of business architectures, fabrics/NoC, processor design, accelerators, or memory hierarchies.
  • Knowledge of high-performance and low-power design techniques.
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