Minimum 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. 3 years of experience with software design and architecture. Experience with either agentic engineering or Linux Kernel, career development, and leadership. Preferred qualifications: Master’s degree or PhD in Engineering, Computer Science, or a related technical field. 8 years of experience with data structures and algorithms. 3 years of experience in a technical leadership role leading project teams and setting technical direction. 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects. Experience in Technical Infrastructure, Release Engineering, and Release Management. 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. In this role, you will be responsible for the Operating System that runs Google, from the Kernel through the Node userspace. The Agentic Engineering team within CoreOS is driving the AI revolution into how we work, test, release, and support compute across Production. This team is 25% exploration, 50% agentic engineering, 25% productionization - but top to bottom revolutionary in providing fast turn-around 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 team 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: $207000 - $301000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google. Responsibilities Drive projects by conceptualizing and validating novel AI-driven strategies to optimize Google’s "Node-level" engineering and global compute performance. Lead, coach, and influence a distributed team of 3-5 Kernel, Node OS, and AI experts, fostering a culture of rapid execution. Design, develop, and maintain large-scale software solutions, ensuring alignment on goals and timelines across cross-functional teams. Debug, fix, and optimize complex issues while staying current with upstream practices to ensure non-disruptive rollouts across the fleet. Build tools and dashboards to monitor production demand and quality, implementing testing to minimize production issues.