NVIDIA
Senior Technical Marketing Engineer - DSX AI Infrastructure Software
$160K–$322K
US, CA, Santa Clara +1 · Senior · Full-time · Posted Oct 5, 2026
- Location
- US, CA, Santa Clara
US, CA, Remote - Experience
- 8+ years in infrastructure engineering, systems engineering, solutions architecture, software engineering, technical marketing engineering, site reliability engineering, or a related role.
- Education
- BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or another technical field, or equivalent experience.
About the job
Deploy and validate DSX-aligned AI infrastructure software stacks, then create technical content, demos, and training that help customers and partners operate AI factories.
What you'll do
- Deploy and test multi-node GPU software stacks, including pre-release software, and report interoperability, resiliency, and usability issues.
- Create practitioner-facing documentation, reference architectures, automation, demos, and training for deployment and operations.
- Support customers, partners, and internal teams through briefings, workshops, events, and direct technical guidance.
What they ask for
- BS or MS in a technical field, or equivalent experience.
- Hands-on experience deploying and operating Linux-based infrastructure, including multi-node GPU systems and production operations.
- Strong working knowledge of Kubernetes and/or Slurm.
- Experience automating deployments and operations, plus examples of technical work for practitioner audiences.
Nice to have
- Experience with NVIDIA DSX, DGX systems, DGX Cloud, NVIDIA AI Enterprise, BlueField DPUs, or DOCA.
- Experience operating large GPU clusters, AI workloads, facilities integrations, or contributing to related open-source communities.
Pay
160,000 USD - 253,000 USD (base salary) for Level 4; 200,000 USD - 322,000 USD (base salary) for Level 5
Tools
Linux, Kubernetes, Slurm, Python, Helm, GitOps, CI/CD, infrastructure-as-code
AI focus
Supports AI factory infrastructure by deploying and operating multi-node GPU systems and enabling dependable AI training and inference workloads.
Model-written summary · the original posting is authoritative