Staff AI Scheduling & Orchestration Engineer

About the role

Bitdeer is looking for a Staff AI Scheduling & Orchestration Engineer to lead the workload placement logic for its AI‑native NeoCloud platform. You will design and operate advanced scheduling fabrics that eliminate GPU stranding and maximize utilization across large compute fleets.

Key responsibilities

• Design and implement batch scheduling architectures using frameworks such as Volcano or YuniKorn for multi‑node gang scheduling.
• Develop cluster‑wide admission control and job queueing mechanisms with Kueue to handle high‑volume AI workloads.
• Leverage Kubernetes Dynamic Resource Allocation and custom scheduler plugins for complex accelerator requests.
• Architect topology‑aware pod placement optimizing NVLink and InfiniBand communication.
• Implement automated GPU sharing (MIG, time‑slicing) and multi‑tenant isolation policies.
• Collaborate with GPU Systems and Storage teams to tightly integrate scheduling with bare‑metal hardware and I/O patterns.
• Drive reliability and scalability of the scheduling stack, resolving contention and deadlock in large‑scale HPC environments.
• Mentor junior engineers and conduct design reviews.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field.
• 6+ years of distributed systems engineering experience with deep hands‑on expertise in Kubernetes scheduling frameworks.
• Extensive experience with AI workload patterns and distributed training frameworks (e.g., PyTorch Distributed, Ray, MPI).
• Proven track record operating, debugging, and scaling scheduling stacks in HPC or large‑scale production cloud environments.
• Strong knowledge of GPU hardware architectures and related scheduling challenges.
• Experience with infrastructure automation and IaC (Terraform, Go‑based Operators).
• Excellent technical communication and leadership skills.

Required skills

• Kubernetes scheduling, Volcano, YuniKorn, Kueue, Dynamic Resource Allocation (DRA)
• GPU technologies: NVLink, InfiniBand, MIG, time‑slicing
• Distributed training frameworks: PyTorch Distributed, Ray, MPI
• Infrastructure automation: Terraform, Go Operators
• HPC and large‑scale cloud environment operations