NVIDIA is spearheading the AI revolution and the creation of state-of-the-art accelerated compute platforms for global utilization. Our Network Architecture, Modeling and Performance Insights group is seeking a skilled and driven AI Network Modeling Architect to conduct advanced network research and optimization while utilizing and enhancing our advanced simulation tools.
In this role, you will be a key contributor to defining the architecture and improving network performance for AI and High Performance Computing (HPC) workloads. You will be responsible for modeling advanced network architectures, topologies, configurations, logic and traffic patterns, and analyzing their effect on end to end performance. You will develop expertise in our simulation tools and contribute directly to their development road map and prioritization, develop core features in the simulator, and expose the capabilities to additional users. You will take part in complex architecture decisions, define and verify the modeling assumptions, and put them to the test vs. real HW and SW performance. If you're passionate about tackling intricate challenges and contributing to comprehensive systems and working on innovative solutions, we want to hear from you.
What you'll be doing:
Directly impact the architecture of NVIDIA’s next generation AI and HPC offerings through modeling, simulation and analysis.
Deep dive into network behavior for training and inference use cases under advanced network topologies, configurations and traffic patterns which represent real life AI workloads, and analyze their effect on the system’s performance. Develop advanced simulation solutions while contributing modular and scalable code.
Actively support the HW and SW development life cycles, map existing and candidate features into the simulation domain to enable their analysis, impact assessment and optimization.
Take full independent ownership of the performance modeling roadmap for a defined set of features. Interface directly with internal clients, communicate the analysis conclusions effectively and iterate on them. Drive projects from concept to completion.
Improve the quality of the simulation as a software product, ensuring robustness and reliability.
What we need to see:
BSc or above in Computer Science, Electrical Engineering, or a related field
5+ years of recent hands-on coding experience with strong proficiency in C++ and Python. You must be comfortable navigating, optimizing, and contributing to a complex, large-scale codebase.
End to end system perspective - capable of bridging the gap between hardware behavior, micro-architecture, and software performance (latency/throughput).
Project ownership capability with proven ability to lead technical initiatives, prioritize features, and manage project lifecycles autonomously with minimal supervision.
Ways to stand out from the crowd:
Strong background in communication networks technology. Deep understanding of Ethernet, NVLink, and/or Infiniband technologies, data center infrastructure, and network protocols.
Familiarity with discrete event simulators (e.g., OMNeT++, SystemC, or proprietary architectural simulators).
Advanced degree or experience focusing on computer architecture, algorithms, networking, or distributed systems.
With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us and, due to unprecedented growth, our special engineering teams are growing fast. If you're a creative and autonomous engineer with a genuine passion for technology, we want to hear from you.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Skills Required
- BSc or above in Computer Science, Electrical Engineering, or related field
- 5+ years recent hands-on coding experience with strong proficiency in C++ and Python
- Comfortable navigating, optimizing, and contributing to large-scale complex codebases
- End-to-end system perspective bridging hardware, micro-architecture, and software performance (latency/throughput)
- Project ownership capability with ability to lead technical initiatives and manage project lifecycles autonomously
- Write production-quality C++ and Python code daily and develop simulator features
- Strong background in communication networks technology (Ethernet, NVLink, InfiniBand)
- Familiarity with discrete event simulators (OMNeT++, SystemC, or similar)
- Advanced degree or experience in computer architecture, algorithms, networking, or distributed systems
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
