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• Engineer Hyper-Scale Cluster Platforms: Enhance Kubernetes-based cluster management systems to deliver superior performance, scalability, and resilience—supporting resource orchestration across client’s infrastructure.

• Advance Unified Scheduling: Design and maintain a comprehensive scheduling framework that supports diverse workloads, including containers, VMs, online services, offline computing, AI/ML, and CPU/GPU-intensive tasks within massive-scale resource pools.

• Develop Intelligent Scheduling Systems: Optimize workload performance and resource utilization across heterogeneous resources—CPU, GPU, memory, network, and power—spanning global data centers.

• Deliver Excellence and Innovation: Produce high-quality, maintainable code while staying ahead of advancements in open-source technologies, AI/ML research, distributed systems, and serverless computing.

• B.S./M.S, degree in Computer Science, Computer Engineering or a related area with 2+ years of relevant industry experience;

• Proven experience designing, architecting and building cloud and infrastructure related but not limited to resource management, allocation, job scheduling and monitoring.

• Familiarity with container and orchestration technologies such as Docker and Kubernetes.

• Proficiency in at least one major programming language such as Python, Go, C++, Rust, and Java.

• Proficient in spoken and written Cantonese/ Mandarin and English.

• Experience in one large scale cluster management systems, e.g., Kubernetes, Ray, Yarn, or Mesos, is highly desirable.

• Experience in large scale resource efficiency management and job scheduling development is highly desirable.

• Project experience in application scaling, workload co-location, and isolation enhancement is highly desirable.

#LPS

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