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vCluster Labs is seeking an AI Infrastructure Engineer to work directly with customers deploying GPU-powered Kubernetes infrastructure at scale. This is a pre-sales technical role where you will lead end-to-end deployments for GPU neocloud and AI Factory customers, from bare metal configuration through production-ready vCluster environments.
You will be among the first technical team members customers engage with, responsible for driving technical deployments, infrastructure optimization, and knowledge transfer. Your work includes configuring and troubleshooting bare metal GPU nodes, CNI configuration, GPU Operator setup, distributed storage backends, and RDMA/InfiniBand connectivity. You will validate Kubernetes and vCluster deployments, ensure customer self-sufficiency, and document reusable playbooks that scale the deployment motion for future customers.
This role bridges Sales and Engineering: you'll participate in pre-sales proof-of-value engagements requiring deep infrastructure expertise while collaborating with the Engineering and Product teams to surface field-discovered infrastructure challenges that inform the roadmap.
vCluster Labs is a venture-backed startup (raised $28M+ from Khosla Ventures and others) headquartered in San Francisco with a distributed, remote-first team. The company powers over 100,000 GPUs and 1 million CPUs across 50+ AI clouds and Fortune 500 companies. vCluster is the open-source platform behind tenant isolation on Kubernetes (11,000+ GitHub stars, 40M+ tenant clusters created since 2021).
REQUIREMENTS:
- 5+ years of experience deploying and operating Kubernetes in production, ideally on bare metal or in high-complexity environments
- Practical knowledge of NVIDIA GPU Operators, CUDA tooling, and systems-level GPU node configuration
- Deep understanding of CNI plugins, overlay networks, load balancing, and connectivity diagnosis in layered environments
- Experience with persistent volume configuration, CSI drivers, and distributed storage systems (Ceph, Rook, Weka, Longhorn)
- Comfort operating in ambiguous, fast-moving environments where you write playbooks in real time
- Preference for modern tech stacks and rejection of legacy approaches
BONUS:
- Automation scripting experience (Bash, Python, Go)
- Kubernetes certifications (CKA) or Kubernetes Operator development experience
- AI/ML familiarity including inference serving, GPU scheduling, and LLM deployment tooling
- Experience building AI automation in documentation and knowledge bases
