Networking AI Technical Lead

Google Google · Big Tech · Sunnyvale, CA +2

This role is for a Technical Lead in Networking at Google, focusing on large-scale software solutions. While it mentions experience with AI/agentic products or their infrastructure as a preferred qualification, the core responsibilities and minimum qualifications are centered around traditional software engineering, infrastructure, and networking. The primary focus is on building and enhancing large-scale software solutions, not directly on developing or deploying AI models or systems as the core function.

What you'd actually do

  1. Provide technical leadership on high-impact projects.
  2. Influence and coach a distributed team of engineers.
  3. Facilitate alignment and clarity across teams on goals, outcomes, and timelines.
  4. Manage project priorities, deadlines, and deliverables.
  5. Design, develop, test, deploy, maintain, and enhance large scale software solutions.

Skills

Required

  • C++
  • software testing
  • software launching
  • large-scale infrastructure
  • distributed systems
  • networks
  • compute technologies
  • storage
  • hardware architecture
  • software design
  • software architecture

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field
  • data structures
  • algorithms
  • technical leadership
  • project teams
  • technical direction
  • complex, matrixed organization
  • cross-functional projects
  • cross-business projects
  • AI/agentic products
  • infrastructure to serve AI/agentic products

What the JD emphasized

  • 8 years of experience programming in C++
  • 5 years of experience testing, and launching software products
  • 5 years of experience building and developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture
  • 3 years of experience with software design and architecture
  • 2 years of experience building AI/agentic products, or infrastructure to serve those products, and understanding the trade-offs and challenges necessary in making AI and agentic workloads a success