Staff Silicon Architect, Deepmind

Google Google · Big Tech · New York, NY +3

This role focuses on architecting, designing, and implementing hardware accelerators for AI workloads within Google DeepMind's GenAI Systems. The position involves co-designing systems across the hardware and software stack, from logic design and architecture to ML model optimization and deployment in data centers. The goal is to deliver production-quality hardware and software products that meet the demands of hyperscale AI.

What you'd actually do

  1. Architect, design, and implement hardware for novel systolic array accelerators.
  2. Own subsystems up to the entire accelerator, carry the implementation of those subsystems from requirements through design sketches, implementation, timing closure, through tapeout, and into bringup and application mapping.
  3. Collaborate with application, algorithm, and performance-tuning experts to codesign solutions for the best efficiency, performance, and programmability.
  4. Write code for accelerators to meet up with production requirements at the system, library, and application levels.
  5. Bring a generalist approach to computer systems beyond specific hardware-related skills, interfacing with colleagues in a wide span of disciplines.

Skills

Required

  • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
  • 10 years of experience with leading multiple design domains (Design For Test, Design Verification and Physical Design).
  • 5 years of experience with logic design, computer architecture, and circuit theory.
  • 5 years of experience in power or performance modeling or system performance analysis.

Nice to have

  • Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
  • 2 year of experience in linux kernel programming.
  • Experience with processor core architectures (such as ARM, x86, RISC-V, etc.) and IPs commonly used in SoC designs.

What the JD emphasized

  • future demands
  • future demands
  • production-quality hardware and software products
  • production requirements

Other signals

  • hyperscale AI competition
  • accelerators
  • codesigned systems
  • ML model design
  • production-quality hardware and software products