Principal Software Engineer - Kubernetes AI Scheduler

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 Principal Engineer for KAI, you will drive the technical direction of the project and shape the future of AI scheduling in the Kubernetes ecosystem.

What You’ll Be Doing

• Define the technical roadmap of KAI-Scheduler, driving its architecture, APIs, extensibility, performance, and long-term direction, ensuring alignment with the evolving Kubernetes ecosystem.
• Drive the scalability strategy of KAI for massive-scale deployments (thousands of nodes and tens of thousands of GPUs), with deep understanding of 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.
• Engage directly with contributors, users, and customers, incorporating feedback from real-world production deployments into the project's technical direction and serving as a visible technical leader within the open-source community.
• Proactively collaborate with related upstream projects (schedulers, AI frameworks, cluster autoscalers, Kubernetes SIGs/WGs, etc.) and represent KAI in community and ecosystem discussions.
  
  

• B.Sc. or M.Sc. in Computer Science or a related field or equivalent experience
• 15+ years of experience in backend software development, including system design and architecture, include 6+ 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 background with experience solving complex optimization and distributed systems problems.
• Superb technical level and a proven ability to mentor other engineers through code reviews, design reviews, and technical leadership.
• Experience in maintaining open-source projects in the Kubernetes or broader cloud-native ecosystem, with a strong understanding of healthy open-source project dynamics, contributor engagement, and community collaboration.