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. 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! This includes customers using NVIDIA GPU clusters, AMD GPU clusters, GPU VMs, and rack-scale bare-metal environments.
You will act as a trusted advisor across LLM training, fine-tuning, RAG workloads, distributed training frameworks, storage throughput requirements, multi-GPU scaling, and performance tuning. This role requires deep technical fluency and exceptional customer management skills to help AI/ML teams achieve predictable, cost-efficient, high-performance outcomes
Key responsibilities
- AI/GPU Onboarding & Workload Architecture Lead onboarding for customers deploying GPU clusters (bare metal, VMs, or hybrid).
- Advise on cluster design: multi-GPU topology, NVLink/NVSwitch considerations, RDMA, Infiniband and RoCE Ethernet, networking throughput, and storage IOPS requirements.
- Guide customers in selecting GPU types and configurations based on workload (training, fine-tuning, inference, embeddings, RAG pipelines).
- Support distributed frameworks: PyTorch, TensorFlow, DeepSpeed, Megatron, JAX, Ray, Mosaic, HuggingFace, etc. Advanced hands on Kubernetes skills Advanced hands on SLURM skills Performance Optimization & Scaling Identify bottlenecks (network, storage, memory bandwidth).
- Provide tuning recommendations for batch size, mixed precision, parallelization strategies, and checkpointing.
- Help customers evaluate cost vs. performance tradeoffs (GPU mix, CPU pairing, instance types, cluster sizing).
- Technical Relationship Ownership Own the long-term technical strategy across assigned GPU/AI accounts, including hyperscalers, labs, and high-growth AI startups.
- Host recurring technical review meetings, roadmap reviews, and optimization sessions.
- Define scaling plans, future GPU reservation needs, and capacity forecasting.
- Incident & Escalation Management Partner with Support, SRE, Networking, NOC, and Product Management & Engineering to resolve high-urgency incidents.
- Manage outage communications, corrective action plans, and postmortem reviews with customers.
- Advocate for GPU reliability improvements and influence roadmap priorities.
- Account Growth & Expansion Identify opportunities for expanded clusters, high speed storage, or networking upgrades.
- Support Sales with technical validation and architecture diagrams needed for expansion.
- Customer Advocacy & Product Feedback Provide structured feedback on existing and future GPU offerings, networking fabrics, storage platforms, and upcoming AI/ML platform features.
- Partner with Product on early access programs (new GPUs, pipelines, orchestration, etc.)