Senior Silicon Design Verification Engineer, Google Cloud

Google Google · Big Tech · Bengaluru, Karnataka, India

This role focuses on the verification of custom silicon solutions, specifically Tensor Processing Units (TPUs), which are designed to accelerate Artificial Intelligence/Machine Learning (AI/ML) workloads. The engineer will be responsible for the full verification life-cycle, including planning, test execution, and coverage closure, ensuring the reliability and performance of these AI/ML-focused hardware components.

What you'd actually do

  1. Plan the verification of digital design blocks and interact with design engineers to identify important verification scenarios.
  2. Identify and write all types of coverage measures for stimulus and corner-cases.
  3. Debug tests with design engineers to deliver functionally correct design blocks.
  4. Measure to identify verification holes and to show progress towards tape-out.
  5. Create a constrained-random verification environment using SystemVerilog and Universal Verification Methodology (UVM).

Skills

Required

  • verification planning
  • test execution
  • coverage closure
  • constrained-random verification environments
  • SystemVerilog
  • Universal Verification Methodology (UVM)
  • RTL level verification
  • Specman/E
  • FPGA verification
  • ASIC verification
  • IP/subsystem/SoC verification
  • networking domain verification
  • packet processing
  • bandwidth management
  • congestion control
  • standard IP components/interconnects verification
  • microprocessor cores verification
  • hierarchical memory subsystems verification

Nice to have

  • computer architecture
  • industry-standard simulators
  • revision control systems
  • regression systems
  • AI/ML accelerators verification
  • vector processing units verification
  • full verification life cycle experience
  • problem-solving skills
  • communication skills

What the JD emphasized

  • meeting stringent AI/ML performance and accuracy goals
  • Artificial Intelligence/Machine Learning (AI/ML) workloads on Tensor Processing Unit (TPU) hardware
  • Artificial Intelligence/Machine Learning (AI/ML) accelerators