Staff AI Scheduling & Orchestration Engineer

Key Responsibilities:

• Design and implement advanced batch scheduling architectures using frameworks like Volcano or YuniKorn to support multi-node gang scheduling.
• Develop and manage cluster-wide admission control and sophisticated job queueing mechanisms utilizing Kueue to manage high-volume AI workload traffic.
• Architect topology-aware pod placement strategies that optimize for low-latency communication via NVLink and InfiniBand fabrics.

Qualifications:

• 6+ years of distributed systems engineering with deep expertise in Kubernetes scheduling frameworks.
• Extensive experience with AI workload execution patterns and distributed training frameworks (e.g., PyTorch Distributed, Ray, MPI).
• Strong knowledge of GPU hardware architectures and scheduling challenges related to distributed AI training.

Our Technology Stack:

• Kubernetes scheduling frameworks (Volcano, YuniKorn, Kueue) and Dynamic Resource Allocation.
• Infrastructure automation with Terraform and Go-based Operators.
• High-performance computing (HPC) environments with NVLink and InfiniBand interconnects.