Staff AI Scheduling & Orchestration Engineer
Bitdeer is a technology company providing Bitcoin mining solutions.
Maintainer signals as of 9/25/2026
Funding history
Investors
Projects
About Bitdeer
Bitdeer provides full-spectrum Bitcoin mining and high-performance computing solutions, including SEALMINER mining equipment, Minerbase cooling containers, cloud mining, co-mining, mining management applications, mining rights marketplaces, and large-scale data center operations. The company also offers AI cloud infrastructure with GPU computing, model training and deployment capabilities, and turnkey AI data center solutions for enterprise customers and developers. Bitdeer is headquartered in Singapore and operates globally.
Skills
Candidate Availability
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