Note: The job is a remote job and is open to candidates in USA. Bitdeer is a technology company focused on AI and Bitcoin mining infrastructure. The company is seeking a Staff AI Scheduling & Orchestration Engineer to lead workload placement logic for its AI-native NeoCloud platform, designing scalable scheduling, admission control, queueing, topology-aware placement, GPU sharing, and multi-tenancy solutions. The role also involves improving reliability, collaborating with GPU Systems and Storage teams, and mentoring engineers.

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
• Leverage Kubernetes Dynamic Resource Allocation (DRA) and custom scheduler plugins to manage complex accelerator requests natively
• Architect topology-aware pod placement strategies that optimize for low-latency communication via NVLink and InfiniBand fabrics
• Implement automated GPU sharing technologies (e.g., MIG, time-slicing) and multi-tenancy isolation policies to maximize cluster-wide utilization
• Collaborate with the GPU Systems and Storage teams to ensure the scheduling layer is tightly integrated with bare-metal hardware and storage I/O patterns
• Drive the reliability and scalability of the scheduling stack, resolving resource contention and deadlock scenarios in large-scale HPC environments
• Mentor junior engineers and conduct design reviews to maintain architectural excellence in our orchestration layer

Skills

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field
• 6+ years of distributed systems engineering, with deep, hands-on expertise in Kubernetes scheduling frameworks and orchestrators
• Extensive experience with AI workload execution patterns and distributed training frameworks (e.g., PyTorch Distributed, Ray, MPI)
• Proven track record of operating, debugging, and scaling scheduling stacks in high-performance computing (HPC) or large-scale production cloud environments
• Strong knowledge of GPU hardware architectures and the specific scheduling challenges related to distributed AI training and inference
• Experience with infrastructure automation and infrastructure-as-code (e.g., Terraform, Go-based Operators)
• Excellent technical communication and leadership skills; ability to influence cross-functional teams and align architectural goals
• Ability to work in a high-velocity engineering environment and translate complex, ambiguous requirements into concrete, scalable engineering solutions

Company Overview