Hardware System Architect, Pixel Watch

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

This role is for a Hardware System Architect for Google's Pixel Watch, focusing on defining and implementing the hardware architecture for wearable products to enable ambient and edge computing applications. The architect will ensure subsystems meet performance, power, and cost goals, and will analyze data processing paths and associated hardware, silicon, and software solutions. The role involves collaboration with internal teams and external partners, and investigating emerging technologies for future products. While the role touches on AI/ML for on-device processing and AI accelerators, its core focus is hardware system architecture for consumer electronics, not AI model development.

What you'd actually do

  1. Architect the data processing paths and hardware-software interfaces for ambient and edge computing applications on wearable hardware.
  2. Define system and component-level specifications for performance, power, and thermals. Support critical user journeys, including on-device AI for audio and ecosystem interoperability across Google’s platforms.
  3. Evaluate the critical trade-offs between power, cost, and form factor. Select chipsets and key components while influencing supplier road maps to deliver optimized solutions.
  4. Oversee system prototyping and integration. Drive the validation and optimization process to ensure all hardware meets established Key Performance Indicators.
  5. Investigate emerging technologies, algorithms, and architectures to drive innovation for future wearable products.

Skills

Required

  • Bachelor's degree in Electrical Engineering, Computer Engineering, Physics, or equivalent practical experience
  • 6 years of experience working in a consumer electronics environment
  • 3 years of experience in technical leadership

Nice to have

  • 10 years of experience in computer architecture and System-on-Chip (SoC) design, for Android-based hardware systems
  • Experience in balancing complex design trade-offs across performance, power, cost, and form factor within hardware and software domains
  • Experience with machine learning models and AI accelerators, such as Neural Processing Units (NPUs)
  • Experience in ambient and edge computing, including on-device AI processing, data processing, and memory management for low-power devices
  • Knowledge of consumer electronics, with a focus on Wearable or Mobile ecosystems
  • Ability to navigate ambiguity and drive technical consensus across matrixed engineering, software, and product management teams

What the JD emphasized

  • on-device AI
  • AI accelerators
  • ambient and edge computing