NVIDIA
Solutions Architect, Virtualization
$184K–$288K
US, CA, Remote +1 · Full-time · Posted Sep 30, 2026
- Location
- US, CA, Remote
US, Remote - Experience
- 8+ years of professional experience across the stated industry backgrounds, including 3-5+ years of direct, hands-on virtualization or virtual desktop infrastructure experience.
- Education
- Bachelor's or Master's degree or equivalent experience in Computer Science, Engineering, or a related field.
About the job
Leads technical engagements with VMware and ecosystem partners to design, validate, and deploy NVIDIA virtualization and vGPU solutions for customer workloads, including AI and data science use cases.
What you'll do
- Co-design scalable architectures and deployment plans with VMware; lead solution design, validation, troubleshooting, and customer support.
- Enable adoption of NVIDIA SDKs, frameworks, systems, and vGPU Compute for AI inference, LLM routing, accelerated data science, and agentic AI.
- Provide customer and partner feedback to NVIDIA product and engineering teams and support joint validation and reference architectures.
What they ask for
- Bachelor's or Master's degree or equivalent experience in Computer Science, Engineering, or a related field.
- Deep expertise in vGPU profiles, Multi-Instance GPU (MIG), GPU passthrough, VM sizing and density, and hypervisor-based delivery.
- Ability to communicate complex technical ideas to technical and non-technical audiences.
- Experience designing, validating, and deploying GPU-accelerated visualization, compute, or AI solutions in virtualized and containerized environments.
Nice to have
- Container-orchestration integration with VMware Tanzu, Red Hat OpenShift, or upstream Kubernetes, including NVIDIA GPU Operator operations.
- Familiarity with Nutanix, Red Hat/OpenShift, Hyper-V, or KVM environments.
Pay
184,000 USD - 287,500 USD (base salary)
Tools
VMware, vGPU, Multi-Instance GPU (MIG), NVIDIA SDKs, VMware Tanzu, Red Hat OpenShift, Kubernetes, NVIDIA GPU Operator
AI focus
Enable vGPU Compute for AI inference, LLM routing, accelerated data science, and agentic AI workloads.
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