This role pays more than
50%
of similar roles. Most pay
$172,725–$247,731
— the shaded band above.
At the midpoint, this role pays about
$206k
versus about
$210k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
As a Senior Technical Marketing Engineer for the DSX AI Infrastructure Software team, you will stand up and validate complete software stacks on multi-node GPU systems to educate the AI factory ecosystem. You will translate complex technical deployments into high-quality content including reference architectures, installation guides, runbooks, and demo videos. Your daily work involves building reusable automation using Python, shell scripting, infrastructure-as-code, Kubernetes, Slurm, Helm, GitOps, and CI/CD pipelines. You will collaborate with engineering and product teams to test pre-release software against training and inference workloads while identifying interoperability issues. The role focuses on the technical challenges of operating an AI factory, specifically addressing provisioning, networking, storage, cluster orchestration, security, and observability. You will provide technical support and assets for partners while ensuring the entire stack functions as a cohesive system.
Stand up and validate complete software stacks on multi-node GPU systems to capture dependencies and configuration requirements.
Create technical content including reference architectures, installation guides, troubleshooting runbooks, code examples, and demo videos.
Develop reusable automation using Python, shell scripting, infrastructure-as-code, Kubernetes, Slurm, and CI/CD pipelines.
Build demos and training materials covering deployment, monitoring, scheduling, fault isolation, and security for AI factories.
Test pre-release software with representative training and inference workloads to provide feedback on interoperability and resiliency.
Create repeatable assets and conduct train-the-trainer sessions to support solution architects, field teams, and partners.
Collaborate with open-source and cloud-native communities to improve documentation and demonstrate integration approaches.
Present technical work at customer briefings, partner workshops, industry events, and webinars.
What we're looking for
BS or MS in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent experience.
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.
Strong working knowledge of Kubernetes and/or Slurm, including containers, operators, Helm charts, cluster lifecycle, and workload scheduling.
Experience in core infrastructure domains like bare-metal provisioning, firmware, networking (Ethernet/InfiniBand), storage, and observability.
Ability to automate deployments through scripting, APIs, configuration management, infrastructure-as-code, Git-based workflows, and CI/CD.
Proven ability to create technical content for practitioners, such as deployment guides, reference architectures, code repositories, and demos.
Experience with NVIDIA DSX, DGX systems, or AI training workloads (preferred).
GPU Architecture
NVLink
NVL72
AI Inference
System Architecture
Liquid Cooling
Power Delivery
Thermal Management
Rack-scale Systems
Accelerated Computing
CPU/GPU Co-design