Senior Accelerators Systems Software Architect, AI Transformation

Google Google · Big Tech · Sunnyvale, CA +1

Senior Software Architect role focused on developing and optimizing the node and distributed system software, firmware, tools, and testing frameworks for integrating Accelerators (GPUs, TPUs) into Google's data centers. The role supports machine learning and high-performance workloads for internal services and Google Cloud customers, influencing the stack from hardware-software interface to AI system deployment.

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++ programming
  • Software design and architecture
  • Software testing and launching
  • Integrating Generative AI tools or LLM interfaces
  • Hardware-software interaction
  • Data center servers and AI platforms

Nice to have

  • Master's degree or PhD in Engineering, Computer Science, or a related technical field
  • Data structures and algorithms
  • Technical leadership
  • Complex, matrixed organization experience
  • Peripheral Component Interconnect Express (PCIe) or other 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 architecture and writing software that interacts with hardware
  • 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