Engineering Tech Lead (vNode)

As an Engineering Tech Lead at vCluster Labs, you aren't just shipping container runtime features; you are defining how Kubernetes operators get VM-grade tenant isolation without the VM tax. vNode replaces virtual kubelets and microVMs with a runtime built on Linux user namespaces and seccomp, and the person in this seat owns where that runtime goes next. You will partner directly with the vNode founding engineers, run the technical bar for the team, and ship the work that decides whether AI Clouds and regulated enterprises can adopt vNode as their default isolation layer. As an Engineering Tech Lead, your role will include: • Owning the vNode technical execution: Drive the architecture for how vNode wraps containerd, integrates with the kubelet, and exposes safe isolation primitives. You will set the bar for what ships, what gets deferred, and what gets redesigned. • Going deep on container runtimes and isolation: Lead the work where vNode meets containerd, Kata Containers, gVisor, runc, and the kernel. You will be the person who can explain (and improve) exactly what happens between a Pod spec and a process running under a constrained user namespace with a tight seccomp profile. • Shipping the kubelet integration surface: Own how vNode plugs into the node lifecycle: CRI, kubelet device plugins, cgroups v2, eviction, and the rough edges between Kubernetes' node model and a runtime that does not assume one tenant per node. • Raising the engineering bar: Run technical design reviews, set the pattern for testing isolation guarantees, and mentor the engineers shipping alongside you. You are not a people manager, but you are the engineer the team copies. • Being Customer Zero for vNode: Run vNode against vCluster Platform tenant clusters internally before customers see it. You will close the loop between what AI Cloud operators need and what vNode actually does in production. • Representing vNode externally: Contribute upstream where it matters (containerd, runc, Kubernetes SIG-Node), write the technical posts that explain why namespace-based isolation is the right answer, and represent vCluster Labs at KubeCon-class venues when the timing is right. This role could be a fit for you if you bring: • Deep container runtime experience: You have shipped production work against containerd directly, not just consumed it through Docker or Kubernetes. Direct experience with Kata Containers, gVisor, or another sandboxed/isolated runtime is a strong plus. • Kubernetes node-level depth: You have worked inside the kubelet, the CRI layer, or a node-resident agent. You know what cgroups v2, OCI hooks, and the kubelet's PLEG do and where they break. • Go systems programming chops: You write production Go for systems-level code (syscalls, namespaces, file descriptors, process lifecycle), not just service handlers. • Linux isolation fluency: User namespaces, seccomp-bpf, capabilities, and Landlock are not abstract concepts; you have shipped against them and can reason about their failure modes. • Tech Lead instincts: You set technical direction by writing the design doc, prototyping the hard part, and then bringing the team along. You raise the bar without becoming the bottleneck. Bonus points for: • Upstream contribution history: Meaningful commits to containerd, runc, Kata, gVisor, Kubernetes SIG-Node, or related projects. • Tenant Isolation domain expertise: You have built or operated infrastructure where the threat model includes hostile workloads on shared hosts (AI Cloud operators, multi-tenant SaaS, regulated industries). • Public technical voice: Talks, posts, or RFCs that move the conversation on container isolation. About vCluster Labs We are a venture-backed tech startup and the company pioneering Kubernetes virtualization for the AI era. We raised +$30M from top-tier VCs such as Khosla Ventures (first investor in OpenAI, GitLab, Stripe, Doordash) and are in a hyper-growth phase looking for motivated people to complement our team. Our headquarters are in San Francisco (Salesforce Tower), but our team is distributed around the globe and we have a remote-first work culture. We are the leading platform for operating GPU infrastructure, enabling AI Cloud providers to deliver a hyperscaler-like experience to their customers and AI factories that need to build that same experience for their internal teams. Our platform delivers the full operational stack operators need to run their GPU data centers - managed Kubernetes, fast isolated tenant provisioning, and automated node provisioning and lifecycle management - enabling them to accelerate time to value, reduce operational burden, and maximize the ROI of every GPU. We're the company behind vCluster, an open-source technology for virtualizing Kubernetes (10k+ GitHub stars, 40M+ virtual clusters created since 2021). Open source is part of our DNA. At KubeCon North America 2025, we launched our Infrastructure Tenancy Platform for AI - a Kubernetes-native f...