Machine Learning System Scheduling Engineer Graduate (Applied Machine Learning) - 2027 Start

What you'll do

• Build scheduling systems supporting Volcano Ark and machine learning platform products.
• Optimize orchestration of heterogeneous compute, storage, and networking resources across clusters.
• Schedule training and inference workloads under multi-tenant isolation to improve resource utilization and efficiency.

What they ask for

• Completing or recently completed a Bachelor's or Master's degree in Computer Science or a related discipline.
• Proficient in one or two programming languages in a Linux environment; examples include Go, Java, or Python.
• Familiar with at least one mainstream machine learning framework and with Kubernetes architecture and ecosystem and container technologies.
• Understand distributed systems principles and have participated in designing, developing, or maintaining large-scale distributed systems.

Nice to have

• Practical experience in large-scale cluster resource scheduling and source-level familiarity with schedulers such as Kubernetes, Volcano, YARN, or Mesos.
• Experience in areas such as CUDA, RDMA, AI infrastructure, high-performance computing, ML for systems, or distributed storage; cloud-native machine learning systems experience is also preferred.

Tools

Linux, Go, Java, Python, TensorFlow, PyTorch, Kubernetes, Docker

AI focus

Supports machine learning training and inference workloads through resource scheduling; the posting does not specify developing AI models or algorithms.