AI Infrastructure Engineer

As vCluster’s AI Infrastructure Specialist, you will work directly with customers at the earliest and most critical stage of their journey: from bare metal GPU nodes through to a production-ready deployment. This is not a traditional professional services role; you operate pre-sale as part of a proof of value engagement scoped to reach production. You will be one of the first team members a neocloud or AI Factory engages with at a technical depth, and the playbooks you develop will scale the motion for the next hire and customer.

vCluster is gaining rapid traction with GPU AI Clouds and enterprises building AI Factories: organizations that need to offer Kubernetes as a managed service on bare metal GPU infrastructure, and need to do it fast. This role exists to make that happen.

As an AI Infrastructure Engineer, your role will include:

• Lead Technical Deployments: Drive end-to-end technical deployments for GPU neocloud and AI Factory customers, from initial bare metal configuration to a validated vCluster environment.
• Infrastructure Optimization: Configure and troubleshoot bare metal GPU node infrastructure, including CNI configuration, GPU Operator setup, distributed storage backends, and RDMA/InfiniBand.
• Validation: Deploy and validate Kubernetes and vCluster to provide GPU-powered managed K8s.
• Knowledge Transfer: Work alongside customer teams to build self-sufficiency, ensuring they can operate and grow the platform independently.
• Scaling through Documentation: Document reusable playbooks and deployment architectures so your learnings become the next customer’s head start.
• Feedback Loop: Collaborate with Engineering and Product to surface recurring infrastructure challenges, acting as a direct feedback loop from the field into the roadmap.
• Strategic Partnering: Join Sales in the pre-sales process where deep infrastructure work is required to achieve a meaningful proof of value.

This role could be a fit for you if you bring:

• Production K8s Mastery: 5+ years of experience deploying and operating Kubernetes in production, ideally on bare metal or in high-complexity environments.
• GPU Fluency: Practical knowledge of NVIDIA GPU Operators, CUDA tooling, and systems-level configuration for GPU nodes.
• Networking Fundamentals: Deep understanding of CNI plugins, overlay networks, load balancing, and connectivity diagnosis in layered environments.
• Storage Expertise: Experience with persistent volume configuration, CSI drivers, and distributed systems like Ceph, Rook, Weka, or Longhorn.
• Operational Agility: Comfort operating in ambiguous, fast-moving environments where you are often writing the playbook in real time.
• Modern Tech Mindset: You thrive in environments that reject legacy tech and prefer a modern stack where you can solve a variety of problems from pipelines to internal services.

Bonus points for:

• Automation Skills: Experience writing automation scripts with Bash, Python, or Go.
• Kubernetes Depth: Relevant certifications such as CKA (Certified Kubernetes Administrator) or experience writing Kubernetes Operators.
• AI/ML Familiarity: Experience with inference serving, GPU scheduling, and the tooling around LLM deployment.
• Documentation: Experience building AI Automation in documentation to contribute to a shared knowledge base.

About vCluster Labs

We’re the #1 platform for AI infrastructure, trusted by the world’s fastest-growing AI cloud builders. We’re a venture-backed startup that’s raised over $28M from top-tier investors including Khosla Ventures (first investor in OpenAI, GitLab, Stripe, and DoorDash), and we’re in a hyper-growth phase looking for motivated people to join our team. Our headquarters are in San Francisco (Salesforce Tower), but our team is distributed around the globe with a remote-first culture.

We give AI Cloud providers and AI factories a hyperscaler-like experience on their own GPU infrastructure. Our platform runs the full stack an operator needs, from bare metal provisioning and node lifecycle management up through managed Kubernetes, Slurm, Ray, and inference clusters, so they can turn raw GPUs into cluster products they can sell in days instead of spending 12+ months building it themselves. Today we power over 100,000 GPUs and 1 million CPUs across 50+ AI clouds and Fortune 500 companies, backed by a team of 40+ infrastructure engineers who build alongs…