Senior AI Scheduling & Orchestration Architect

Bitdeer Technologies Group; Singapore; On-site; SGD 180,000 - 240,000; Full time

Benefits offered by this job

Welfare benefits

Training & mentoring

Startup spirit

Job summary

Bitdeer Technologies Group is seeking an experienced Distributed Systems Engineer to lead the design and optimization of our batch scheduling stack for multi-node AI workloads and HPC environments. You will work with Volcano/YuniKorn, Kueue, and GPU-aware scheduling to achieve low-latency, high-throughput AI pipelines.

You will mentor engineers, collaborate with GPU and storage teams, and shape the orchestration layer in a high-velocity, growth-focused setting.

Qualifications

• 6+ years of distributed systems engineering experience.
• Deep, hands-on expertise in Kubernetes scheduling frameworks and orchestrators.
• 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 HPC or large-scale production cloud environments.
• Strong knowledge of GPU hardware architectures and scheduling challenges in distributed AI training/inference.
• Experience with infrastructure automation (Terraform, Go-based Operators).
• Excellent technical communication and leadership skills; able to influence cross-functional teams.

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 for high-volume AI workloads.
• Leverage Kubernetes Dynamic Resource Allocation and custom scheduler plugins to manage complex accelerator requests.
• Architect topology-aware pod placement strategies for low-latency communication via NVLink and InfiniBand.
• Implement automated GPU sharing technologies and multi-tenancy isolation policies to maximize cluster utilization.
• Collaborate with GPU Systems and Storage teams to integrate scheduling with bare-metal hardware and I/O patterns.
• Drive reliability and scalability of the scheduling stack, resolving resource contention and deadlocks in large-scale HPC environments.
• Mentor junior engineers and conduct design reviews to maintain architectural excellence in our orchestration layer.

Skills

Distributed systems

Kubernetes scheduling

AI workload patterns

GPU hardware knowledge

Education

CS/EE degree

Tools

PyTorch Distributed

Ray

MPI

Job description

Bitdeer Technologies Group is seeking an experienced Distributed Systems Engineer to lead the design and optimization of our batch scheduling stack for multi-node AI workloads and HPC environments. You will work with Volcano/YuniKorn, Kueue, and GPU-aware scheduling to achieve low-latency, high-throughput AI pipelines.

You will mentor engineers, collaborate with GPU and storage teams, and shape the orchestration layer in a high-velocity, growth-focused setting.