Senior Technical Marketing Engineer - DSX AI Infrastructure Software

NVIDIA•Santa Clara, CA

10d•$200,000 - $322,000•Hybrid

About The Position

NVIDIA is seeking a Senior Technical Marketing Engineer to demonstrate and educate the AI factory ecosystem on bringing up and operating the full NVIDIA DSX stack. The stack spans facilities and multi-node GPU infrastructure, provisioning, networking, storage, cluster orchestration, security, observability, and workload enablement. The role involves standing up and validating DSX-aligned software stacks, developing practitioner-focused technical content and automation, and creating demonstrations, labs, and training for the practical operation of AI factories. The engineer will demonstrate how infrastructure and software layers work together, test pre-release software with representative training and inference workloads, and provide actionable feedback to Product and Engineering. The role also supports solution architects, field teams, cloud and OEM partners, ISVs, and system integrators through reusable assets, training, demonstrations, and direct support. It includes engaging with open-source and cloud-native communities, prioritizing content and product improvements based on recurring feedback, and presenting at customer briefings, partner workshops, industry events, webinars, and internal training. Some travel is required.

Requirements

Bachelor's or master's degree in Computer Science, Computer Engineering, Electrical Engineering, or another technical field, or equivalent experience.
At least 8 years of experience in infrastructure engineering, systems engineering, solutions architecture, software engineering, technical marketing engineering, site reliability engineering, or a related role.
Hands-on experience deploying and operating Linux-based data center, cloud, HPC, or AI infrastructure, including multi-node GPU systems and production operational practices.
Strong working knowledge of Kubernetes and/or Slurm, including containers, operators, Helm charts, cluster lifecycle, and workload scheduling.
Experience in several core infrastructure domains, such as bare-metal provisioning, firmware and drivers, compute, Ethernet or InfiniBand networking, storage, identity, multi-tenancy, secrets or certificate management, telemetry, observability, and fleet health.
Ability to automate deployments and operations using scripting, APIs, configuration management, infrastructure-as-code, Git-based workflows, and CI/CD.
Examples of technical work for practitioner audiences, such as deployment guides, documentation, reference architectures, code repositories, demos, workshops, blog posts, conference talks, or training.
Excellent written, verbal, and visual communication skills, including the ability to explain complex systems and defend technical recommendations to business and technical partners.
Ability to balance multiple projects and constituents, prioritize under tight deadlines, and work across Engineering, Product, Field, Marketing, and partner teams.

Nice To Haves

Experience with NVIDIA DSX, DGX systems, DGX Cloud, NVIDIA AI Enterprise, BlueField DPUs, DOCA, or related NVIDIA infrastructure software.
Experience operating large GPU clusters and diagnosing distributed performance, networking, storage, scheduling, or hardware-health issues.
Experience with AI training and inference workloads and the requirements for operating them dependably on accelerated infrastructure.
Experience connecting infrastructure software to facilities or operational technology systems, including power, cooling, and building management systems.
Active participation in cloud-native, HPC, infrastructure automation, or open-source communities, including published examples or project contributions.

Responsibilities

Stand up and validate complete DSX-aligned software stacks on multi-node GPU systems, documenting dependencies, configuration order, validation steps, and operational handoffs.
Create technical content from working deployments, including reference architectures, quick-starts, installation and upgrade guides, troubleshooting runbooks, code examples, blogs, whitepapers, and demo videos.
Build reusable examples and automation using APIs, Python or shell scripting, infrastructure-as-code, containers, Kubernetes, Slurm, Helm, GitOps or equivalent tools, and CI/CD where appropriate.
Develop demos, labs, and training addressing AI factory operations, including deployment, tenant setup, upgrades, monitoring, scheduling, fault isolation, remediation, capacity management, and security.
Demonstrate how data center hardware, infrastructure and cluster management software, orchestration, AI platforms, and workloads operate as one system, collaborating with TME, Product, Engineering, and Marketing.
Test pre-release software with representative training and inference workloads, assess interoperability and resiliency, identify issues, and provide feedback to Product and Engineering.
Help solution architects, field teams, cloud and OEM partners, ISVs, and system integrators use the stack through repeatable assets, train-the-trainer sessions, live demos, and direct support on important engagements.
Collaborate with open-source and cloud-native communities to demonstrate integrations, address documentation and usability shortcomings, and help partners expand and develop the DSX software stack.
Use recurring customer, partner, field, and developer feedback to prioritize content and recommend product improvements; track effects on deployment time and operational success.
Present work in customer briefings, partner workshops, industry events, webinars, and internal training; travel as required.