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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 drive end-to-end deployments from bare metal GPU nodes through production-ready vCluster environments for neocloud and AI Factory customers.
You will be among the first technical team members customers engage with, responsible for leading technical deployments, optimizing bare metal GPU infrastructure, validating Kubernetes and vCluster configurations, and transferring knowledge to ensure customer self-sufficiency. A key part of this role is documenting reusable playbooks and deployment architectures that become the foundation for scaling the motion to future customers.
Responsibilities include:
- Lead end-to-end technical deployments for GPU neocloud and AI Factory customers, from bare metal configuration through validated vCluster environments
- Configure and troubleshoot bare metal GPU node infrastructure, including CNI configuration, GPU Operator setup, distributed storage backends, and RDMA/InfiniBand
- Deploy and validate Kubernetes and vCluster to provide GPU-powered managed Kubernetes
- Work alongside customer teams to build self-sufficiency and operational independence
- Document reusable playbooks and deployment architectures to scale the engagement model
- Collaborate with Engineering and Product to surface infrastructure challenges and inform roadmap priorities
- Partner with Sales in pre-sales processes requiring deep infrastructure work to achieve proof of value
vCluster Labs is the #1 platform for AI infrastructure, trusted by the world's fastest-growing AI cloud builders. The company is venture-backed (raised $28M+ from Khosla Ventures and others), headquartered in San Francisco, and operates with a remote-first culture. The platform powers over 100,000 GPUs and 1 million CPUs across 50+ AI clouds and Fortune 500 companies. vCluster is the open-source technology behind tenant isolation on Kubernetes with 11,000+ GitHub stars and 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 configuration for GPU nodes
- Deep understanding of CNI plugins, overlay networks, load balancing, and connectivity diagnosis in layered environments
- Experience with persistent volume configuration, CSI drivers, and distributed 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 ability to solve problems across pipelines and internal services
BONUS QUALIFICATIONS:
- Automation skills with Bash, Python, or Go
- Kubernetes certifications (CKA) or experience writing Kubernetes Operators
- Experience with inference serving, GPU scheduling, and LLM deployment tooling
- Experience building documentation and knowledge bases
