AI Cluster Architect

vultr · Remote

Who We Are Vultr is on a mission to make high-performance cloud infrastructure easy to use, affordable, and locally accessible for enterprises and AI innovators around the world. With 33 global cloud data center locations, Vultr is trusted by hundreds of thousands of active customers across 185 countries for its flexible, scalable, global Cloud Compute, Cloud GPU, Bare Metal, and Cloud Storage solutions. In December 2024 Vultr announced an equity financing at a

.5 billion valuation. Founded by David Aninowsky and self-funded for over a decade, Vultr has grown to become the world’s largest privately-held cloud infrastructure company. Vultr Cares Excellent Medical Benefits w/ 100% company-paid premiums for employee only plan + 100% company-paid dental & vision premiums 401(k) plan that matches 100% up to 4% with immediate vesting Professional Development Reimbursement of

Careeroza — One-stop Zone for Aspirants

Study material, Careeroza mentorship, tech jobs, and career guidance on careeroza.com.

Public study materials

,500 each year 11 Holidays + Paid Time Off Accrual + Rollover Plan + take your birthday off Commitment matters to Vultr! Increased PTO at 3 year & 10 year anniversary + 1 month paid sabbatical every 5 years + Anniversary Bonus each year $500 first year remote office setup + $400 each following year for new equipment Internet reimbursement up to $75 per month Gym membership reimbursement up to $50 per month Company-paid Wellable subscription Join Vultr Vultr is looking for an AI Cluster Architect who will be responsible for creating and refining large-scale GPU cluster architectures within strict power and infrastructure limits. This role focuses heavily on power-aware design: starting from a fixed power envelope, the architect determines the optimal number of GPUs while accounting for the full stack of services needing to be deployed—compute nodes, storage systems, networking fabric, cooling, and facility constraints. This role requires deep experience navigating heterogeneous environments, multiple generations of hardware, and end user requirements. The architect must understand how different GPU