Staff Software Engineer, Platform Networking Google Distributed Cloud - Google
Sunnyvale, CA
About the Job
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development, and with data structures/algorithms.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 5 years of experience in cloud networking, network architecture (enterprise, data center, WAN), or system architecture with a track record of leading networking development initiatives.
- 2 years of experience with Kubernetes or other container orchestration frameworks.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 3 years of experience in a technical leadership role leading project teams and setting technical direction.
- 3 years of experience working in an organization involving cross-functional or cross-business projects.
- Knowledge of cloud platforms and global networking concepts (e.g., CDN, Anycast, Global Load Balancing), with experience with multi-tenancy and multi-zone architectures.
- Experience with physical and host networking infrastructure within distributed data center environments, as well as network operations, troubleshooting, and utilizing network monitoring tools.
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
The GDC Platform Networking team builds the high-performance network foundation, and designs and manages network architectures for both GDC versions, encompassing physical networking, SDN, and Kubernetes networking. This ensures connectivity, secure segmentation, and critical services like load balancing. In this role, you will lead network architecture across all layers for both GDC connected and air-gapped.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
The US base salary range for this full-time position is $189,000-$284,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Lead Google Distributed Cloud networking architecture across all layers.
- Lead SDN-driven converged networking architecture for connected and air-gapped multi-vendor networks.
- Drive AI workloads with network offload and high performance.
- Utilize virtualization, multi-tenancy (VPC), and multi-zone architectures.