Senior Software Engineer - Kubernetes AI Scheduler

Job Requisition ID

JR2020152

Job Category

Engineering

Time Type

Full time

KAI-Scheduler is an open-source CNCF project focused on delivering the best scheduling experience for AI workloads on Kubernetes. Adopted by AI frontier labs, leading enterprises, and some of the largest AI infrastructure deployments in the world, KAI helps organizations efficiently run AI at scale.

KAI is designed to support any AI infrastructure—from the latest GPU and networking technologies to future hardware generations—while maximizing performance, utilization, and scalability. As a Senior Software Engineer for KAI, you will help build the future of AI scheduling in the Kubernetes ecosystem, working on challenging problems spanning workload scheduling, Kubernetes internals, and large-scale AI infrastructure.

What You’ll Be Doing

• Develop clean, maintainable, and well-tested software in Go.
• Design and implement scalability improvements for KAI, helping it operates efficiently in massive-scale deployments (thousands of nodes) while addressing Kubernetes scaling constraints and bottlenecks.
• Apply strong algorithmic thinking to solve complex AI workload scheduling and placement challenges, balancing performance, fairness, cluster utilization, topology constraints, and scalability.
• Conduct code and design reviews to uphold high-quality standards and mentor team members.
• Work closely with contributors, users, and customers, helping translate feedback from production deployments into product and engineering improvements.
• Collaborate with related upstream projects (schedulers, AI frameworks, cluster autoscalers, Kubernetes SIGs/WGs, etc.) and contribute to community and ecosystem discussions.
  
  

• B.Sc. or M.Sc. in Computer Science or a related field or equivalent experience
• 8+ years of experience in backend software development, including system design and architecture
• 4+ years of advanced Kubernetes development experience, including designing and implementing CRDs and controllers, with deep expertise in Kubernetes internals, networking, storage, and cluster architecture.
• Strong algorithmic skills with experience tackling complex optimization and distributed systems challenges.
• Strong technical skills and a proven ability to collaborate with and mentor other engineers.