Senior Accelerators Systems Software Architect, AI Transformation

Google Google · Big Tech · Sunnyvale, CA +1

Senior Software Architect role focused on developing and optimizing the software stack for AI accelerators (GPUs, TPUs) within Google's data centers. This involves firmware, drivers, and system software to enable high-performance machine learning workloads for internal services and cloud customers. The role requires deep expertise in hardware-software interaction, system design, and leveraging AI tools for development.

What you'd actually do

  1. Provide technical leadership on high-impact projects, anchoring the architecture and engineering roadmap of common software for Accelerator platforms.
  2. Design, develop, test, deploy, and maintain large-scale software solutions, including board and chip firmware and Linux kernel drivers.
  3. Interface and drive industry ecosystem engagement related to various technology standards for modern Accelerator platforms.
  4. Facilitate and drive AI transformation across Accelerator software teams by leveraging the latest AI tools to speed up design and development.
  5. Influence and coach a distributed team of engineers while managing project priorities, deadlines, and deliverables.

Skills

Required

  • C++
  • Software architecture
  • Software design
  • Software testing
  • Software deployment
  • Firmware development
  • Linux kernel drivers
  • Hardware-software interface
  • Data center servers
  • AI platforms
  • Generative AI tools integration
  • LLM interface integration

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field
  • Data structures
  • Algorithms
  • Technical leadership
  • Cross-functional project management
  • Peripheral Component Interconnect Express (PCIe)
  • High-speed IO protocols
  • ML SOC architecture

What the JD emphasized

  • 8 years of experience programming in C++
  • 5 years of experience with design and architecture; and testing/launching software products
  • Experience integrating Generative AI tools or Large Language Model (LLM) interfaces into workflows
  • Experience with data center servers and AI platforms

Other signals

  • Enabling machine learning and high-performance workloads
  • Influence the entire stack, from the hardware-software interface and computer architecture to the deployment of advanced AI systems
  • Support for AI models and AI transformation