Senior Technical Marketing Engineer - DSX AI Infrastructure Software
Company: Nvidia
Location: US, CA, Santa Clara (Remote)
Salary: $160k - $253k per year
Type: Full-time
Remote: Yes
Posted: 2026-08-26
About this role
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
NVIDIA DSX brings together facilities infrastructure, hardware, software, simulation, and partner technologies to build and run efficient AI factories. We are looking for a Senior Technical Marketing Engineer to show and educate our AI factory ecosystem how to bring up and operate the entire stack, ranging from facilities and multi-node GPU infrastructure to provisioning, networking, storage, cluster orchestration, security, observability, and workload enablement.
What you'll be doing:
- Stand up and validate complete DSX-aligned software stacks on multi-node GPU systems. Capture the dependencies, configuration order, validation steps, and operational handoffs as you go.
- Turn working deployments into useful technical content: reference architectures, quick-starts, installation and upgrade guides, troubleshooting runbooks, code examples, blogs, whitepapers, and demo videos.
- Build reusable examples and automation with APIs, Python or shell scripting, infrastructure-as-code, containers, Kubernetes, Slurm, Helm, GitOps or equivalent experience, and CI/CD where they fit.
- Build demos, labs, and training that address the practical aspects of operating an AI factory, from initial deployment and tenant setup to upgrades, monitoring, scheduling, fault isolation, remediation, capac...