GPU Cloud Platform Engineer

What you'll do

• Build and operate GPU clusters, troubleshoot infrastructure issues, and benchmark multi-node performance.
• Deploy and orchestrate large models across Kubernetes multi-cluster environments, including scaling and load balancing.
• Develop GPU scheduling, monitoring, and multi-cluster management systems, and coordinate infrastructure deployments with data center providers.

What they ask for

• Bachelor's degree or higher in Computer Science, Software Engineering, Electronic Engineering, or a related field.
• 3+ years of experience in system engineering or DevOps.
• 5+ years of experience in cloud-native development or AI engineering, including at least 2 years in Kubernetes multi-cluster management and orchestration.
• Hands-on experience with Kubernetes tools and operations, Docker, monitoring tools such as Prometheus and Grafana, and cloud platforms such as AWS, GCP, or Azure.

Nice to have

• Experience with cluster management tools such as Ray, Slurm, KubeSphere, Rancher, or Karmada; distributed file systems; or high-performance communication protocols.
• Experience with MaaS or inference clusters, CUDA and NCCL, inference optimization, or frameworks such as Triton, vLLM, and SGLang.

Tools

Kubernetes, kubectl, Helm, Docker, Prometheus, Grafana, AWS, GCP

AI focus

Supports AI infrastructure and deployment of large models, including LLMs and video generation models, across GPU clusters; the posting also describes preferred experience in model inference and optimization.