Software Engineer, PhD, Google Cloud Platform

Minimum qualifications:

• PhD degree in Computer Science, Electrical Engineering, or a related technical field.
• Experience in software development, with a focus on areas such as large-scale distributed systems, AI/ML, networking, data storage, or security.
• Experience in architecture or development of distributed systems, including concurrency, multi-threading, or synchronization.
• Experience in algorithms, complexity analysis, and system design.
• Experience coding in one of the following programming languages including but not limited to: C, C++, Python Java or Golang.

Preferred qualifications:

• PhD degree in Computer Science or related technical field with coding experience in C++/Go.
• Experience architecting and developing large-scale distributed systems.
• Experience in concurrency, multithreading and synchronization and good understanding of networking concepts, protocols (e.g., TCP/IP, BGP).
• Strong problem-solving skills and understanding of algorithms and their level of complexity.
• Strong software and systems troubleshooting and debugging skills.
• Good written and verbal communication skills.

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.

As a key member of a small and versatile team, you design, test, deploy and maintain software solutions.

AI Data's mission is to empower Google to rapidly deliver innovative, impactful, safe and compliant AI offerings through high-quality data, infrastructure, and advanced data science. As part of the trust organization in AI data, we build scalable and automated infrastructure to manage ML assets at Google from development to launch with full traceability and auditability, without compromising developer velocity.

As a PhD Software Engineer, your specialized research expertise will be instrumental in solving complex, real-world problems on an unprecedented scale.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s 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.

Responsibilities

• Be responsible for design, development, and delivery of product features and components within Google Cloud Platform (GCP) hybrid connectivity products. Drive projects from design to launch with limited oversight.
• Design, and implement highly scalable, reliable, and secure distributed systems for hybrid cloud networking. Make sound technical judgments in ambiguity and proactively identify opportunities for system improvements or new tool development.
• Collaborate across multiple Engineering teams, including Product Management, Site Reliability Engineering (SRE), and other Networking teams within GCP.
• Contribute to team-level technical decisions and participate in broader engineering community efforts such as interviewing and knowledge sharing.
• Ensure the production health, monitoring, and on-call support for hybrid connectivity services. Drive initiatives to improve system reliability, observability, and supportability, potentially leveraging AI/ML for enhanced operational insights.