AI Infrastructure Engineer — Production-Ready GPU K8s

vCluster Labs; San Francisco (CA); Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Competitive Salary

Premium Insurance

Flexible Working Schedule

Workplace Flexibility

Job summary

vCluster Labs is seeking an AI Infrastructure Specialist to work with customers from bare metal GPU nodes to production deployments. You will operate pre-sale as part of a proof of value engagement and help scale playbooks for future customers.

The role focuses on end-to-end GPU infrastructure, Kubernetes on bare metal, CNI, GPU Operators, and high-performance networking/storage with a remote-first culture and global team.

Qualifications

• 5+ years deploying Kubernetes in production, ideally bare metal.
• Hands-on with NVIDIA GPU Operators and CUDA tooling.
• Experience with CNI, storage backends, and RDMA/InfiniBand.
• Ability to write reusable deployment playbooks.

Responsibilities

• Lead end-to-end technical deployments for GPU neocloud and AI Factory customers.
• Configure and troubleshoot bare metal GPU node infrastructure including CNI, GPU Operator, storage backends, and RDMA/InfiniBand.
• Deploy and validate Kubernetes and vCluster to provide GPU-powered managed K8s.
• Collaborate with customer teams to build self-sufficiency and platform growth.
• Document reusable playbooks and deployment architectures for future customers.
• Provide feedback to Engineering and Product to surface infrastructure challenges.
• Support pre-sales in the proof-of-value process with deep infrastructure work.

Skills

Kubernetes

GPU CUDA

Networking

Storage

Automation

Documentation

Modern Stack

Operational Agility

Tools

CUDA tooling

RDMA

Ceph

Rook

Longhorn

NVIDIA Operator

Job description

vCluster Labs is seeking an AI Infrastructure Specialist to work with customers from bare metal GPU nodes to production deployments. You will operate pre-sale as part of a proof of value engagement and help scale playbooks for future customers.

The role focuses on end-to-end GPU infrastructure, Kubernetes on bare metal, CNI, GPU Operators, and high-performance networking/storage with a remote-first culture and global team.