Senior Software Engineer, Infrastructure, CoreOS Agentic Engineering

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 CoreOS team is responsible for the Operating System that runs Google, from the Kernel through the Node OS. The Agentic Engineering team within CoreOS is driving the AI revolution into how we work, test, release, and support the CoreOS across Production. This team is 25% exploration, 50% engineering, 25% productionization - but top to bottom revolutionary in fast turn-around in developing critical services that could only be possible via AI.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Conceptualize and prove novel AI strategies to optimize Google's compute infrastructure and meet performance needs.
• Build tools for kernel quality insights and manage the non-disruptive rollout of fixes to the fleet in alignment with upstream practices.
• Design tests and debug a broad range of kernel issues across testing, qualification, and release cycles to minimize production impact.
• Provide technical leadership and mentorship to team members within the engineering and operational environments.
• Work within a team of Kernel and AI experts to drive forward the industry and Google's Node-level engineering practice.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience working with the Linux kernel, including debugging and fixing kernel issues.
• 3 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
• 1 year of experience in a technical leadership role.
• Experience with either AI or Linux kernel development.
• Experience developing accessible technologies.
• Experience in Unix/Linux systems, IP networking, performance and application issues with the knowledge of Continuous Integration (CI)/Continuous Deployment (CD) best practices.
• Proficiency in algorithms, data structures, complexity analysis and software design.