GPU Solutions Engineer
Sales
WFA Digital Insight
Vultr’s GPU Solutions Engineer sits at the crossroads of cutting‑edge cloud infrastructure and real‑world AI workloads. Unlike generic cloud engineering roles, this position demands deep familiarity with both bare‑metal GPU hardware and virtualized cloud delivery, while also acting as a trusted advisor to enterprise customers. The day‑to‑day blend of solution architecture, hands‑on troubleshooting, and product feedback makes it a unique conduit between customers and Vultr’s engineering team. Candidates will need to translate complex GPU compute concepts into actionable designs and guide projects from prototype to production, all while navigating a fast‑paced, fully remote environment that rewards proactive ownership.
Job Description
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!
- Create innovative Solutions and Architectures to solve complex Customer use cases.
- Establish relationships within Customers independent of Account Executives.
- Educate customers on the value that Vultr provides and expand their horizons about the art of the possible.
- Participate in deep architectural discussions and design exercises to create world-class solutions at Vultr.
- Collaborate with Engineering and Product Management to deliver Voice of the Customer to guide Product and Feature development.
- Lead resolution of complex customer challenges.
- Partner with Marketing to create reference architectures, white papers, workshops, and demonstrations for internal and external uses.
- Travel 25% Qualifications - Highly motivated / self-starter with a sense of ownership, willingness to learn, and desire to succeed.
- A collegial and collaborative approach working across Departments and Seniority levels within Vultr’s Customers and Vultr.
- Skilled at influencing, guiding, and facilitating stakeholders and peers with decision making.
- Ability to articulate technical and business concepts to diverse stakeholders.
- Demonstrated willingness and ability to dig into unfamiliar territories to solve complex challenges.
- Experience with structured sales engagement models MEDDPIC, Sandler, BANT, etc.
- Expertise in the GPU ecosystem from GPU Hardware, Bare Metal and Virtualized Cloud Delivery, AL & ML frameworks and AI & ML Ops orchestration.
- Extensive knowledge of AI Models / Algorithms, Libraries, Compilers and Runtimes for diverse silicon ecosystems.
- GPU benchmarking and workload testing experience.
- Demonstrated experience with workload orchestration, Kubernetes and Slurm.
- System and Cluster level understanding of x86 server hardware architecture and Linux OS.
- Hands on with Infrastructure as Code methodologies including, Terraform, Ansible, etc.
- Networking experience, including knowledge of Infiniband, RoCE / UEC Ethernet, or other networking protocols.
- NCCL / RCCL performance optimization.
- High Performance Storage experience, knowledge of performant multi-user offerings including Open Source and COTS options.
- 5+ years of design, implementation, or consulting in applications and infrastructure experience - 4+ years of Solution Engineering, Sales Engineering, Professional Services or Consulting experience - 3+ year of experience deploying GPU-based AI, ML & Analytics solutions (e.g., for Training, Inference, Fine Tuning, Reinforcement Learning, Agentic Harnesses) Compensation $180,000 - $200,000 Final compensation will vary depending on years of experience, background/skill set, location, and applicable laws.
How to Stand Out
- Highlight any hands‑on projects where you built or optimized GPU workloads on Kubernetes; include performance metrics if possible.
- Prepare to discuss specific AI/ML frameworks you’ve used and how you integrated them with cloud infrastructure.
- Demonstrate familiarity with sales qualification models (MEDDPIC, Sandler, BANT) during interviews.
- Bring examples of technical documentation you authored—whitepapers, demos, or workshop materials.
- When negotiating, emphasize the remote‑office stipend and professional‑development budget as part of the total compensation package.
- Watch for overly vague descriptions of day‑to‑day responsibilities; ask interviewers how success is measured for this role.
- Ensure your resume reflects both deep technical expertise and the ability to translate that into business outcomes.
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