Staff AI Scheduling & Orchestration Engineer
Bitdeer Technologies Group is a technology company providing Bitcoin mining solutions, mining hardware, data-center infrastructure, and AI cloud services. It serves individual, institutional, and enterprise customers globally.
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About Bitdeer Technologies Group
Bitdeer provides vertically integrated Bitcoin mining and high-performance computing services. Its operations include mining equipment procurement and manufacturing, datacenter design and construction, equipment management, daily mining operations, cloud mining, and mining-related services. The company also offers AI cloud infrastructure and high-performance computing powered by NVIDIA GPUs for AI and machine-learning workloads, serving customers across global markets.
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Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will lead workload placement and scheduling logic for large-scale AI training and inference. You will build batch scheduling and queueing systems, develop accelerator-aware placement strategies, implement GPU sharing and tenant isolation, integrate scheduling with hardware and storage, resolve contention and deadlocks, and guide architecture through reviews and mentoring.
Requirements
- Bachelor’s or Master’s degree in Computer Science Electrical Engineering or a related field
- 6+ years of distributed systems engineering experience
- Deep expertise in Kubernetes scheduling frameworks and orchestrators
- Experience with AI workloads and PyTorch Distributed Ray or MPI
- Experience operating debugging and scaling production or HPC scheduling stacks
- Strong knowledge of GPU hardware architectures
- Experience with infrastructure automation and infrastructure-as-code
- Excellent technical communication and leadership skills
- Ability to translate ambiguous requirements into scalable engineering solutions
Responsibilities
- Design batch scheduling architectures with Volcano or YuniKorn
- Develop admission control and job queueing with Kueue
- Use Kubernetes DRA and custom scheduler plugins for accelerator requests
- Architect topology-aware placement for NVLink and InfiniBand
- Implement GPU sharing with MIG and time-slicing
- Define multi-tenancy isolation policies
- Integrate scheduling with GPU hardware and storage systems
- Resolve resource contention and deadlocks in HPC environments
- Mentor engineers and conduct design reviews