Senior AI Scheduling & Orchestration Engineer

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

About Bitdeer:

Bitdeer is a world-leading technology company for Bitcoin mining and AI cloud.

Bitdeer is committed to providing comprehensive Bitcoin mining solutions for its customers. Apart from designing industry-leading ASIC chips and manufacturing mining rigs, the Group handles complex processes involved in computing across the value chain. This includes equipment procurement, transport logistics, datacenter design and construction, equipment management, and network and facility operations. Bitdeer also offers advanced cloud capabilities to customers with a high demand for artificial intelligence.

Headquartered in Singapore, Bitdeer operates globally with a diversified 3 GW energy portfolio, and deploys Bitcoin mining and HPC datacenters in the United States, Bhutan, Norway, Canada, Malaysia, and Ethiopia.

What you will be responsible for:

• 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.

How you will stand out:

• 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.

What you will experience working with us:

• A culture that values authenticity and diversity of thoughts and backgrounds;
• An inclusive and respectable environment with open workspaces and exciting start-up spirit;
• Fast-growing company with the chance to network with industrial pioneers and enthusiasts;
• Ability to contribute directly and make an impact on the future of the digital asset industry;
• Involvement in new projects, developing processes/systems;
• Personal accountability, autonomy, fast growth, and learning opportunities;
• Attractive welfare benefits and developmental opportunities such as training and mentoring.

Bitdeer is committed to providing equal employment opportunities in accordance with country, state, and local laws. Bitdeer does not discriminate against employees or applicants based on conditions such as race, colour, gender identity and/or expression, sexual orientation, marital and/or parental status, religion, political opinion, nationality, ethnic background or social origin, social status, disability, age, indigenous status, and union.