GPU Solutions Engineer
$180,000–$200,000 year
RemoteUnited States
United StatesRemoteFull Time$180,000–$200,000 yearMedium
Full TimeMedium
Job Summary
Engage with customers to understand objectives and create innovative architectures for complex GPU use cases. Establish independent relationships within client organizations and lead resolution of challenging technical challenges. Collaborate with Engineering and Product teams to deliver Voice of the Customer insights for feature development. Partner with Marketing to create reference architectures, white papers, workshops, and demonstrations. Educate clients on Vultr's value proposition and guide them from initial design through production deployment. Travel 25% of the time.
Required 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)
- Travel 25%
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