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 our products across the full stack – from applications and software libraries to hardware architecture and physical design.
What Youll 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.
