Staff AI Scheduling & Orchestration Engineer
Bitdeer Technologies Group is a technology company providing Bitcoin mining solutions, SEALMINER mining equipment, cloud mining, AI cloud infrastructure, high-performance computing, and data-center services. Its customers include miners, developers, AI and machine-learning users, and institutional customers.
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About Bitdeer Technologies Group
Bitdeer Technologies Group operates across Bitcoin mining, AI cloud, and data-center infrastructure. Its offerings include proprietary SEALMINER equipment, Minerbase cooling systems, cloud-mining plans, co-mining services, mining-farm management tools, and mobile apps. The company also provides GPU-based AI and high-performance computing services for model training and deployment, as well as turnkey AI data-center solutions. Bitdeer is headquartered in Singapore and operates globally, with major operations in the United States, Bhutan, Norway, Canada, Malaysia, and Ethiopia.
Skills
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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