NVIDIA is building the world’s most advanced AI computing platforms, powering breakthroughs in generative AI, large language models, and scientific discovery. Our accelerated computing technologies enable researchers, engineers, and enterprises to push the boundaries of what is possible with artificial intelligence. We are seeking an AI Networking Architect to join the Networking Research Group. This role will help bridge the gap between emerging tasks supported by advanced technologies and the data center infrastructure that powers them. In this role, you will work at the intersection of AI applications, distributed systems, networking hardware, and software architecture.
You will join a focused team of multidisciplinary engineers driving AI workload optimization through deep application understanding, network analysis, and end-to-end systems thinking. Your insights will directly shape NVIDIA products across the full stack - from applications and software libraries to hardware architecture and physical design.
What You’ll Be Doing:
Model the performance of complex AI workloads to identify bottlenecks and recommend system-level optimizations.
Analyze brand-new AI models, distributed training techniques, and inference workloads to understand their infrastructure requirements.
Build Platforms, simulations and HW platforms, execute AI workloads and build analytical tools to evaluate trade-offs across compute, memory, storage, and network behavior.
Translate research insights and workload behavior into actionable software, hardware, and networking architecture requirements.
Partner with architecture, software, and product teams to influence future NVIDIA networking and AI infrastructure roadmaps.
Drive architectural innovation by applying deep workload analysis to real-world advanced machine learning frameworks.
What we need to see:
B.Sc. Or M.Sc. in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
3+ years of relevant industry or research experience.
Strong machine learning or data science background, with hands-on experience in LLMs, generative AI, or deep learning systems.
Strong systems-level thinking, capable of estimating end-to-end requirements across the AI stack.
Shown ability to translate research findings and product requirements into clear software and hardware specifications.
Excellent research skills, including the ability to digest academic papers, self-learn new domains, and independently test hypotheses.
Advanced programming skills for performance modeling, data analysis, and prototyping.
Excellent communication skills, demonstrating proficiency in presenting complex technical findings clearly and confidently.
Ways to Stand Out from the crowd:
Experience with distributed training, distributed inference, or large-scale AI serving systems.
Experience in Agentic programming, and AI tools
Familiarity with GPU clusters, collective communication, storage systems, or AI networking bottlenecks.
NVIDIA is home to some of the most innovative and dedicated professionals in the industry. We are committed to fostering a diverse work environment and are proud to be an equal-opportunity employer.
Skills Required
- B.Sc. or M.Sc. in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience
- 3+ years of relevant industry or research experience
- Hands-on experience with LLMs, generative AI, or deep learning systems
- Strong systems-level thinking, capable of estimating end-to-end requirements across the AI stack
- Ability to translate research findings and product requirements into software and hardware specifications
- Excellent research skills, ability to digest academic papers, self-learn new domains, and independently test hypotheses
- Advanced programming skills for performance modeling, data analysis, and prototyping
- Excellent communication skills, ability to present complex technical findings clearly and confidently
- Experience with distributed training, distributed inference, or large-scale AI serving systems
- Experience in Agentic programming and AI tools
- Familiarity with GPU clusters, collective communication, storage systems, or AI networking bottlenecks
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.”
