About the job
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.
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.
Qualifications
Minimum
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
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.