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 $3.5 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

100% company-paid insurance premiums for employee medical, dental and vision plans.

401(k) plan that matches 100% up to 4%, with immediate vesting

Professional Development Reimbursement of $2,500 each year

11 Holidays + Paid Time Off Accrual + Rollover Plan

Commitment matters to Vultr! Increased PTO at 3 year and 10 year anniversary + 1 month paid sabbatical every 5 years + Anniversary Bonus each year

$500 stipend for remote office setup in first year + $400 each following year

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 SKUs, NICs, switches, and fabrics interact at scale, including their individual and aggregate power and thermal characteristics. They will evaluate multi-plane, rail-optimized, and tiered fabric designs across technologies like InfiniBand, RoCE, and SpectrumX to ensure the networking architecture supports the intended GPU count without overrunning facility limits or switch radix and/or topology constraints. This role balances customer-specific requirements for compute, storage, and service density, ensuring that the final cluster design maintains acceptable levels of GPU and fabric performance, while maximizing the number of usable GPUs within the total power budget.

Key Responsibilities

Architect large-scale GPU clusters within fixed site power budgets that optimizes for maximum GPU density while reserving necessary headroom for compute services, storage, and networking.

Model and validate power consumption across the full cluster bill of materials (GPUs, CPUs, NICs, switches, fabric components, storage, and facility limits).

Evaluate tradeoffs across multiple fabric networking architectures (InfiniBand, RoCE, SpectrumX) as well as multi-plane, 2-tier/3-tier, and rail-optimized topologies.

Determine network scale limits based on switch radix, link speed, topology, and blocking requirements.

Gather, interpret, and maintain detailed SKU-level power and thermal specifications for GPUs, NICs, switches, DPUs, storage, and server platforms.

Develop power-aware cluster configuration templates and capacity-planning models that can scale across sites with varying constraints and allow for quick iteration and ideation.

Document architecture, design choices, tradeoff analyses, and operational considerations for deployment and lifecycle management.

Provide guidance on future-proofing, including the ability to incorporate next-gen GPUs, NICs, or fabrics.

Collaborate with vendors on novel fabric architectures that enable large-scale cluster deployments (100k+ GPUs)

Qualifications

7+ years designing or building large-scale HPC, AI, or hyperscale GPU clusters.

Expert understanding of GPU and accelerator system design, including node topology, PCIe/NVLink/NVSwitch/ROCm, and NIC-to-GPU affinity considerations.