Senior Rtl Design Engineer, Google Cloud

Google Google · Big Tech · Bengaluru, Karnataka, India

This role focuses on the design and implementation of Application-Specific Integrated Circuits (ASICs) for accelerating Machine Learning (ML) computations in data centers, specifically for Google's AI and Infrastructure team. The engineer will work on micro-architecture, implementation, and optimization of these custom silicon solutions, collaborating with various teams to deliver high-performance and efficient hardware accelerators.

What you'd actually do

  1. Own micro-architecture and implementation of complex subsystems.
  2. Work with Architecture, Firmware and Software teams to drive feature closure and develop microarchitecture specifications.
  3. Drive design methodology, libraries, debug, code review in coordination with other IPs Design Verification (DV) teams and physical design teams.
  4. Identify and drive Power, Performance and Area improvements for the domains owned.

Skills

Required

  • 8 years of experience in Application-Specific Integrated Circuit/System on a chip (ASIC/SoC) development with Verilog/SystemVerilog.
  • Experience in micro-architecture and design of IPs and Subsystems.
  • Experience in ASIC design verification, synthesis, timing/power analysis, and Design for Testing (DFT).

Nice to have

  • Master's or PhD degree 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 high performance and low power design techniques.
  • Knowledge of bus architectures, fabrics/NoC, processor design, accelerators, or memory hierarchies.

What the JD emphasized

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

Other signals

  • developing custom silicon solutions that power the future of Google's direct-to-consumer products
  • accelerate Machine Learning (ML) computation in data centers
  • design of ASICs to accelerate ML computation