Senior AI Performance Network Architect

Responsibilities

• Model the performance of complex AI workloads to identify bottlenecks and recommend system-level optimizations.
• Analyze new AI models, distributed training techniques, and inference workloads to understand their infrastructure requirements.
• Build simulation and hardware platforms, run real AI workloads on them, and develop analytical tools to evaluate trade-offs across compute, memory, storage, and network behavior.
• Translate research insights and workload behavior into actionable software, xhardware, 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 production machine learning frameworks.
• What we need to see
• B.Sc. or M.Sc. in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
• 5+ years of relevant industry or research experience. This is not an entry-level position; coursework and personal projects do not substitute for production or research experience at scale.
• Hands-on experience running and analyzing AI workloads on multi-node systems — distributed training or large-scale inference — including measuring where time and resources are actually spent.

Languages