Staff AI Scheduling & Orchestration Engineer

Bitdeer Technologies Group • San Jose, CA

1d

About The Position

Bitdeer is a world-leading technology company for AI and Bitcoin mining infrastructure. Bitdeer is committed to providing comprehensive Bitcoin mining solutions for its customers and building AI computational infrastructure to support the AI revolution. Bitdeer handles complex processes involved in computing such as equipment procurement, transport logistics, data center design and construction, equipment management, and daily operations. Bitdeer also offers advanced cloud capabilities to customers with high demand for artificial intelligence. Headquartered in Singapore, Bitdeer has deployed data centers across multiple countries, including the United States, Norway, Bhutan, and Ethiopia. We are seeking a Staff AI Scheduling & Orchestration Engineer to lead the workload placement logic that defines our AI-native NeoCloud platform. Standard Kubernetes scheduling is insufficient for the demands of large-scale AI; you will be responsible for eliminating "GPU stranding" and maximizing utilization across our expensive compute fleets. This role is pivotal in building a high-performance scheduling fabric that understands the physical realities of our hardware—from NVLink-connected GPU topologies to InfiniBand interconnects. You will work at the intersection of distributed systems and AI, driving the architectural decisions that enable our platform to handle massive-scale distributed training and inference jobs with industry-leading efficiency.

Requirements

• 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.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior