Silicon Design Verification Lead, Google Cloud

Google Google · Big Tech · Bengaluru, Karnataka, India

Lead Silicon Design Verification for Google Cloud's custom hardware, focusing on AI/ML workloads on TPUs. Responsibilities include full verification life-cycle management, team leadership, and ensuring reliability for AI/ML applications.

What you'd actually do

  1. Plan the verification of digital design blocks by fully understanding the design specification and interacting with design engineers to identify important verification scenarios.
  2. Lead a team of chip engineers, plan tasks, allocate people, and hold verification design/code reviews.
  3. Perform code development for critical verification features and capabilities.
  4. Work closely with system, software, design, and physical implementation stakeholders to make technical decisions and represent project status throughout the development process.
  5. Lead design verification efforts and work with external and internal partners.

Skills

Required

  • hardware verification languages (e.g., System Verilog/Specman)
  • design verification methodologies
  • chip design flow
  • creating detailed chip or subsystem design verification strategies and plans
  • leading design verification teams through the full cycle, from concept to delivery

Nice to have

  • Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
  • verifying digital systems using standard IP components/interconnects (e.g., microprocessor cores, hierarchical memory subsystems).
  • verification techniques and the full verification lifecycle.
  • performance verification of Application-Specific Integrated Circuits (ASICs) and ASIC components.
  • ASIC standard interfaces and memory system architecture.
  • multiple System on a Chip (SOC) projects/cycles in three or more products.

What the JD emphasized

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