Senior AI Performance Network Architect
Salary not listed • Israel, Tel Aviv • Full-time
Posted 1mo ago
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
NVIDIA is seeking an AI Networking Architect to join the Networking Research Group. This role focuses on bridging the gap between emerging AI workloads and the data center infrastructure that powers them, operating at the intersection of AI applications, distributed systems, networking hardware, and software architecture. You will contribute to a team driving AI workload optimization through deep application understanding, network analysis, and end-to-end systems thinking. Your contributions will directly influence NVIDIA products across the entire stack, from applications and software libraries to hardware architecture and physical design.
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, and develop 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 production machine learning frameworks.
Requirements
- B.Sc. or M.Sc. in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
- 5+ years of relevant industry or research experience.
- Hands-on experience running and analyzing AI workloads on multi-node systems, including distributed training or large-scale inference.
- Demonstrated performance analysis work, including building and validating analytical or simulation models.
- Strong systems-level thinking across the full AI stack.
- Track record of translating research findings and workload analysis into concrete software and hardware specifications.
- Strong programming skills in Python and C/C++ for performance modeling, data analysis, and prototyping.