Graphics/machine Learning Hardware Ip Silicon Architect

Google Google · Big Tech · Mountain View, CA +2

This role focuses on architecting and integrating custom silicon solutions for Google's consumer products, specifically targeting hardware IP for Graphics, Multimedia, and Machine Learning acceleration. The individual will collaborate with software and hardware teams to define and deliver ASIC IP architectures that meet power, performance, and area targets, from initial design through to product launch. Experience with low-power ASIC hardware IP for complex SoCs, particularly in Graphics Processing and Machine Learning Acceleration, is preferred.

What you'd actually do

  1. Define and deliver the hardware Multimedia/Graphics ASIC IP integration architecture that meet competitive power, performance, area and image quality targets, which will require owning the targets through to tape-out and product launch.
  2. Collaborate with Graphics, Camera, Video, Display and ML software, system and algorithm engineers to co-develop and specify competitive hardware IP architectures for integration into complex SoCs.
  3. Partner with GPU, TPU, camera ISP, video and display hardware IP design teams across global sites to drive the hardware IP architecture specifications into design implementation for complex SoCs.
  4. Align with SoC and System/Experience architects on meeting power, performance and area requirements at the SoC level for Graphics, ML, and Multimedia use cases and experiences.

Skills

Required

  • Bachelor's degree or equivalent practical experience within ASIC Design and Hardware Architecture
  • 10 years of work experience in ASIC Hardware architecture and silicon design

Nice to have

  • Master's degree or PhD in Computer Science or Electrical Engineering
  • architecting and designing low power ASIC hardware IP for complex SoCs in the following areas: Graphics Processing and Machine Learning Acceleration
  • collaborating cross-functionally with Product Management, SoC architecture, IP design and verification, camera, video, Graphics/ML algorithm and software development teams
  • architecting ambient or always-on vision hardware and workflows for ultra low power SoC applications
  • micro architecture, power and performance optimization
  • interconnect/fabric, security, multi-level caching architectures

What the JD emphasized

  • owning the targets through to tape-out and product launch
  • Graphics Processing and Machine Learning Acceleration

Other signals

  • custom silicon solutions
  • hardware experiences
  • Google AI, Software, and Hardware
  • hardware to make computing faster
  • Machine Learning Acceleration
  • Graphics/ML algorithm