Customer Success Engineer

About the role

As a Customer Success Engineer, you will act as the primary architect of customer outcomes, bridging technical depth and customer strategy. You will drive adoption, manage onboarding, and ensure customers realize the full value of their investment while identifying expansion opportunities. You will serve as a proactive partner to platform engineering and DevOps teams, ensuring long-term success and strategic alignment.

Responsibilities

• Lead end-to-end customer onboardings including architecture workshops and platform installation.
• Build and maintain customer success plans that document business objectives and adoption milestones.
• Translate platform adoption into business outcomes such as cost efficiency and engineering velocity.
• Own the customer relationship cadence through regular check-ins and executive business reviews.
• Provide technical guidance on deployment patterns, RBAC, and tenant isolation.
• Manage renewal motions and identify expansion opportunities within existing accounts.
• Represent the voice of the customer internally to the product and engineering teams.

Required skills

• Customer Success
• Technical Account Management
• Kubernetes
• Cloud-native technologies
• RBAC
• Tenant Isolation
• Cloud infrastructure

Nice to have

• CKA
• CKAD
• AI/ML infrastructure
• GPU compute environments
• Internal developer platforms
• Financial services compliance
• Healthcare compliance
• Public sector compliance
• Open-source contribution

Qualifications

• Proven experience in a technical post-sales role such as customer success, technical account management, or professional services

Certifications

• CKA
• CKAD

Benefits

• Competitive salary
• Equity
• Health insurance
• Dental insurance
• Vision insurance
• Life insurance
• Flexible working schedule

About the Company

vCluster Labs is a venture-backed startup and the creator of vCluster, an open-source technology for tenant isolation on Kubernetes. They provide an infrastructure tenancy platform for AI, ML, and GPU-intensive workloads, powering over 100,000 GPUs across various enterprises.