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Location
In office (Bengaluru)
Seniority
Senior · 8+ years exp

Confirmed on the employer's own hiring board on Nov 5, 2026. First seen by Alion on Nov 4, 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

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

In this role, you will own the full verification life-cycle from verification planning and test execution to coverage closure, with an emphasis on meeting stringent AI/ML performance and accuracy goals, build constrained-random verification environments capable of exposing corner-case bugs and ensuring the reliability of Artificial Intelligence/Machine Learning (AI/ML) workloads on Tensor Processing Unit (TPU) hardware. You will collaborate closely with design and verification engineers in active projects and perform verification.

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

  • Plan the verification of digital design blocks and interact with design engineers to identify important verification scenarios.

  • Identify and write all types of coverage measures for stimulus and corner-cases.
  • Debug tests with design engineers to deliver functionally correct design blocks.
  • Measure to identify verification holes and to show progress towards tape-out.
  • Create a constrained-random verification environment using SystemVerilog and Universal Verification Methodology (UVM).

Qualifications

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 Verification, verifying digital logic at RTL level using SystemVerilog or Specman/E for Field Programmable Gate Arrays (FPGAs) or ASICs.
  • Experience in verification and debug of IP/subsystem/SoCs in the networking domain such as packet processing, bandwidth management, congestion control.

  • Experience verifying digital systems using standard IP components/interconnects (e.g., microprocessor cores, hierarchical memory subsystems).

Preferred qualifications:

  • Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
  • Experience with industry-standard simulators, revision control systems, and regression systems.
  • Experience in Artificial Intelligence/Machine Learning (AI/ML) Accelerators or vector processing units.
  • Experience with the full verification life cycle.
  • Excellent problem-solving and communication skills.
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