below marketmost similar roles pay hereabove market
This role pays more than
51%
of similar roles. Most pay
$184,581–$235,750
— the blue 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
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
The Senior Technical Marketing Engineer - GPU and System Architecture joins the accelerated computing product team to serve as a technical voice for GPU and rack-scale systems. You will bridge architecture and deployment by translating complex system designs into narratives for hyperscalers, OEMs, and system IT operators. Daily responsibilities include developing content for GPU architecture deep dives, demonstrating NVL72, NVLink Switching, and CPU/GPU co-design, and explaining infrastructure technologies like power delivery, liquid cooling, and thermal management. You will produce technical blogs, whitepapers, and presentations while addressing design challenges and communicating ROI. The role requires expertise in GPU architecture, accelerators, servers, racks, and cooling. You must possess a BS/MS in Computer Engineering or Electrical Engineering and experience in AI and data center domains.
Silicon Development
Generative AI
Data Center Architecture
Accelerated Computing
Inference
Data Analytics
C2C Fabric
Software Development
AI Infrastructure
Technical Product Marketing
Benchmarking
GPU Technologies
Neural Networks
Transformer Architectures
GPU-accelerated Computing
Data Science
Real-time Rendering
Ray Tracing
Linux
Command-line Tooling
AI Tools
Software QA
PC Hardware Architecture
Model Inference
Performance Testing
Game Development
GPU Optimization
AI Infrastructure
High-Performance Computing
GPU Systems
Data Center Infrastructure
Accelerated Computing
AI/ML
Rack-scale AI Infrastructure
NVIDIA SDKs
HPC
Deployment Validation
Hardware Engineering
Networking