Principal GPU Memory Simulation Architect
NVIDIAJob Title
Principal GPU Memory Simulation Architect
Role Summary
Design and develop next-generation GPU memory and on-chip interconnect performance and functional models, including AI-assisted model generation and large-scale architectural simulation. Work with multidisciplinary engineering teams to define innovative features, validate performance, and translate product requirements into architectural solutions.
This role focuses on building and validating scalable simulation infrastructure to evaluate architectural choices and optimize GPU memory and interconnect subsystems.
Experience Level
Senior — 15+ years of relevant professional experience.
Responsibilities
Key responsibilities include architecture definition, model development, performance analysis, and cross-team collaboration.
- Develop, implement, and refine network-on-chip (NOC) performance and functional models using modern AI techniques to evaluate architectural choices and predict system behavior.
- Define and architect features for next-generation GPU memory and on-chip interconnect subsystems.
- Analyze benchmarks, application workloads, and simulation/emulation results to identify architecture optimizations.
- Collaborate with hardware and software engineering teams to translate product requirements into architectural and implementation plans.
- Build, validate, and maintain large-scale architectural simulation infrastructure and workflows.
Requirements
Core capabilities and experience required for the role.
Must-have:
- 15+ years of relevant professional experience in architectural simulation or related fields.
- Proven performance modeling experience and ability to build and validate architectural simulation infrastructure at scale.
- Experience in large-scale software development projects; strong programming skills in C/C++ and Python or similar scripting languages.
- Deep GPU architecture experience, specifically memory systems and on-chip interconnect design.
- Excellent written and verbal communication skills for collaboration across teams and with external partners.
Nice-to-have:
- Background in parallel computing, datacenter architecture, or large-scale interconnect architecture.
- Experience writing, running, and analyzing test cases within performance modeling frameworks.
- Experience using AI tools for code development, validation, and analysis.
Education Requirements
Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field — or equivalent practical experience.
About the Company
Company: NVIDIA
Headquarters: Santa Clara, California, USA
NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.
