AI Infrastructure Software Lead for Large-Scale Data Centers

Google; Sunnyvale (CA); On-site; USD 207,000 - 300,000; Full time

Job summary

Google's Sunnyvale-based team seeks a Staff Software Engineer focusing on data center and AI infrastructure. You will help build switch software powering Google’s data center and AI platforms, driving high-performance networking technologies at scale.

The role requires ownership, problem solving, and collaboration across teams to advance Gemini and Google Cloud, with opportunities to influence hardware and software integration for AI workloads.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 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.
• Experience in problem management, problem solving, network architecture, and network design.

Responsibilities

• Provide technical leadership on high-impact projects.
• Influence and coach a distributed team of engineers.
• Scale and performance tuning of a large-scale network.
• Design, develop, test, deploy, maintain and improve switch software.
• Work on projects enabling AI networking infrastructure, enabling high performing fabrics for TPUs and GPUs.

Skills

C++ programming

Software testing

Distributed systems

Network architecture

Problem solving

Education

Bachelor's degree or equivalent practical experience

Master’s or PhD in Engineering/CS

Tools

Linux

SDN

Job description

Google's Sunnyvale-based team seeks a Staff Software Engineer focusing on data center and AI infrastructure. You will help build switch software powering Google’s data center and AI platforms, driving high-performance networking technologies at scale.

The role requires ownership, problem solving, and collaboration across teams to advance Gemini and Google Cloud, with opportunities to influence hardware and software integration for AI workloads.