Customer Success Engineer

As a Customer Success Engineer at vCluster, you aren't just managing accounts — you are the primary architect of customer outcomes after the deal closes. Sitting at the intersection of technical depth and customer strategy, you ensure our customers — from hyper-growth AI Clouds to Global Fortune 500 enterprises — re...

You are not a reactive support resource. You are a proactive partner who drives adoption, identifies expansion opportunities, and spots churn before it becomes visible. You build deep relationships with Platform Engineering leaders, DevOps teams, and executive buyers alike — and you can talk credibly with all of the...

Onboarding Ownership: Lead end-to-end customer onboardings — from the sales handoff through to project close. You coordinate architecture workshops, platform installation, access setup, integration configuration, and operational readiness reviews. You set the tone for how customers experience vCluster from day one.

Success Planning: Build and maintain customer success plans that document business objectives, KPIs, adoption milestones, and a forward plan. You treat the success plan as a living document that drives every customer conversation, not a one-time deliverable that sits in a folder.
Value Articulation: Translate platform adoption into business outcomes. You connect Tenant Cluster growth, provisioning time savings, and developer self-service enablement to the metrics your customers care about — cost efficiency, engineering velocity, risk reduction, and platform ROI. When it's time to renew or expand, the business case is already built.
Proactive Engagement: Own the customer relationship cadence — regular check-ins, Quarterly Business Reviews, and Executive Business Reviews. You do not wait for the customer and you run executive reviews that reinforce strategic partnership, not just feature updates.
Technical Credibility: Hold credible technical conversations with platform engineers, DevOps leads, and architects. You understand the vCluster architecture well enough to discuss Tenant Cluster deployment patterns, RBAC, Tenant Isolation, integrations, and Day 2 operations. You can independently assist the customer with their technical challenges and design optimal deployment architectures.
Renewal and Expansion: Own the renewal motion for your accounts — starting 90+ days out. You recognise expansion triggers — new use cases, AI/GPU workloads, additional Tenant Clusters, tier upgrades — and you work with the Account Executive and Solutions Engineer to convert them into growth that solves real customer problems.
Customer Advocacy: Serve as the internal voice of your customers. You represent their feature requests, escalate blockers, and provide actionable feedback to Product and Engineering grounded in real account context — not anecdote.
Proven Post-Sales Experience: You have experience in a technical post-sales role — customer success, technical account management, or professional services — with a track record of driving adoption and measurable business outcomes across a portfolio of technical customers.
Technical Credibility: You have hands-on familiarity with Kubernetes and cloud-native technologies. You can engage with platform engineers on architecture questions, discuss RBAC and Tenant Isolation patterns, and hold your ground in a conversation about cloud infrastructure without bluffing. You know when to bring in deeper technical resources.
Stakeholder Range: You can run an executive business review with a Head of Technology in the morning and troubleshoot an onboarding question with a platform engineer in the afternoon. You adapt your communication without losing depth or credibility in either direction.
Structured and Proactive: You operate with a clear framework — success plans, adoption milestones, renewal timelines — and you don't need to be reminded to use them. You identify risk before the customer tells you about it.
Commercial Awareness: You understand what drives renewal and expansion in a SaaS or platform business. You surface growth opportunities that solve real customer problems, not just opportunities that look good on a pipeline report.
Kubernetes certification (CKA or CKAD) or equivalent demonstrated technical depth in cloud-native infrastructure.
Experience with AI/ML infrastructure, GPU compute environments, or customers building internal developer platforms at scale.
Background working with regulated industries — financial services, healthcare, or public sector -- where compliance and governance add complexity to technical decisions.
Contributions to the Kubernetes or open-source ecosystem.
Experience at a Series B-D startup where you've operated with autonomy and helped build a repeatable post-sales motion from the ground up.
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 framework purpose-built for running AI, ML, and GPU-intensive workloads anywhere, with an NVIDIA-validated reference architecture for DGX systems.