below marketmost similar roles pay hereabove market
This role pays more than
52%
of similar roles. Most pay
$167,500–$243,050
— the blue band above.
At the midpoint, this role pays about
$206k
versus about
$205k
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
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
The Senior Technical Marketing Engineer joins the DSX AI Infrastructure Software team to educate the AI factory ecosystem on operating the full software stack. You will stand up and validate software stacks on multi-node GPU systems, capturing dependencies and configuration orders to create reference architectures, troubleshooting runbooks, and demo videos. Key responsibilities include building reusable automation and labs for provisioning, networking, storage, and cluster orchestration. You will utilize Python, shell scripting, infrastructure-as-code, containers, Kubernetes, Slurm, Helm, GitOps, and CI/CD to solve practical operational challenges. The role focuses on the technical integration of data center hardware, cluster management software, and AI platforms. You will test pre-release software using training and inference workloads to identify interoperability issues and provide feedback to Product and Engineering teams.
Stand up and validate complete DSX-aligned software stacks on multi-node GPU systems.
Create technical content including reference architectures, installation guides, troubleshooting runbooks, and demo videos.
Build reusable automation and examples using Python, shell scripting, Kubernetes, Slurm, and Infrastructure-as-Code.
Develop labs and training materials covering deployment, monitoring, scheduling, and capacity management for AI factories.
Test pre-release software using representative training and inference workloads to identify bugs and interoperability issues.
Provide direct support and train-the-trainer sessions for solution architects, field teams, and cloud partners.
Collaborate with open-source and cloud-native communities to demonstrate practical integration approaches and improve documentation.
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, or site reliability engineering.
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, networking, storage, identity, telemetry, and fleet health.
Ability to automate deployments and operations through scripting, APIs, infrastructure-as-code, Git-based workflows, and CI/CD.
Proven track record of creating technical work for practitioners, such as deployment guides, reference architectures, code repositories, or demos.
Experience with NVIDIA DSX, DGX systems, or AI training and inference workloads (preferred).
AI Infrastructure
HPC
Data Center Systems
Cloud Platforms
DGX
HGX
DGX Cloud
Omniverse
BlueField
DOCA
NVIDIA AI Enterprise
Orchestration
Simulation
Networking
Storage
Reference Architectures
Generative AI
RAG
Agentic AI
LLM
Kubernetes
CI/CD
Git
NVIDIA AI Enterprise
NIM
NeMo
TensorRT
Triton Inference Server
RAPIDS
CUDA
Helm
APIs
SDKs
Cloud-native
Docs-as-code
Remote (Santa Clara, CA)105 days ago
$160,000–$253,000
GPU Architecture
NVLink
NVL72
AI Inference
System Architecture
Liquid Cooling
Power Delivery
Thermal Management
CPU/GPU Co-design
Accelerated Computing
Rack-scale Systems
NVIDIA GPUs
CUDA
Isaac Sim
Isaac Lab
Holoscan
ROS
ROS2
Gazebo
Jupyter Notebooks
GitHub
Physical AI
Medical Imaging
Surgical Robotics
Simulation
Orchestration
open-source