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Salary
≈ $39k – $92k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Staff · 10+ years exp

Confirmed on the employer's own hiring board on Sep 26, 2026. First seen by Alion on Sep 15, 2026.

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 join a team developing Application-Specific Integrated Circuits (ASICs) to accelerate machine learning computation in data centers. You will collaborate with Architecture, Verification, Power and Performance, and Physical Design teams to specify and deliver quality designs for next-generation accelerators. You will solve technical problems through micro-architecture innovation and evaluate design trade-offs between performance and power.

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

  • Lead a team of Engineers in the design organization to deliver complex IPs or Subsystems. Communicate and work with multi-disciplined and multi-site teams.
  • Define micro-architecture (uArch) specifications and block-level design documents, including interface protocols, block diagrams, transaction flows, and pipeline details.
  • Perform RTL development, subsystem integration, and System on a Chip (SoC) integration, as well as Lint, CDC, FV, and UPF checks.
  • Participate in simulation debug, synthesis, timing and power closure, and silicon bring-up.
  • Participate in test plan and coverage analysis of the block and SOC-level verification.

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/SoC development with Verilog/SystemVerilog.
  • Experience in micro-architecture and design of IPs and Subsystems.
  • Experience in ASIC/SoC design verification, synthesis, timing/power analysis, and Design for Testing (DFT).

Preferred qualifications:

  • Master's or PhD degree in Electrical Engineering, Computer Engineering, or Computer Science, with an emphasis on computer architecture.
  • Experience with coding languages (e.g., Python or Perl).
  • Experience in System on a Chip (SoC) designs and integration flows.
  • Knowledge of arithmetic units, bus architectures, processor design, accelerators, or memory hierarchies.
  • Knowledge of high-performance and low-power design techniques.
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