The role involves managing and extending large-scale Kubernetes clusters to support the training and serving of frontier AI models. Responsibilities include scaling the Kubernetes control plane and developing custom scheduling plugins to handle complex, topology-sensitive workloads. The position requires building and maintaining core cluster services to ensure high availability and performance under significant pressure. Additionally, the role entails collaborating with research and infrastructure teams to translate workload requirements into platform capabilities. The work focuses on maintaining system reliability and correctness as the organization's compute footprint expands.

Candidates must possess significant software engineering experience in building and operating production distributed systems. Proficiency in at least one systems-appropriate language such as Go, Python, Rust, or C++ is required, along with deep, hands-on experience with Kubernetes internals. Applicants should demonstrate an ability to debug complex issues across the stack and a track record of designing for system reliability. A bachelor's degree or an equivalent combination of education, training, and experience is the minimum educational requirement.