AI Workload and Networking Research Architect

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 Workload & Networking Architect to join the Networking Research Group and help bridge the gap between cutting-edge AI workloads 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 state-of-the-art AI models, distributed training techniques, and inference workloads to understand their infrastructure requirements.
• 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.
• Rapidly learn new AI domains, including LLMs, generative models, multimodal systems, and emerging AI workloads, and distill key findings for technical teams.
• Drive architectural innovation by applying deep workload analysis to real-world deep learning systems.
• Build models, simulations, and analytical tools to evaluate trade-offs across compute, memory, storage, and network behavior.
  
  

• M.Sc. or Ph.D. 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, with the ability to estimate end-to-end requirements across the AI stack.
• Proven 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 Python programming skills for performance modeling, data analysis, and prototyping.
• Excellent communication skills, with the ability to present complex technical findings clearly and confidently.
• Pragmatic and impact-driven approach: detail-oriented, but able to prioritize the most critical issues.
  
  

• Deep understanding of data center infrastructure, network topologies, and communication protocols.
• Experience with distributed training, distributed inference, or large-scale AI serving systems.
• Knowledge of AI performance metrics and the impact of different deployment strategies.
• Experience extrapolating academic research into tangible software, hardware, or architecture requirements.
• Familiarity with GPU clusters, collective communication, storage systems, or AI networking bottlenecks. Track record of leading complex, multidisciplinary research projects with measurable production impact.