NVIDIA is looking to solve the problem of defining the next generation of products for high growth markets in Machine Learning, Artificial Intelligence, and High-Performance Computing, specifically in the data center segment.
Requirements
- Deep understanding of fundamentals of GPU architecture, Machine Learning, Deep Learning, and LLM architecture with ability to articulate relationship between application performance and GPU and data center architecture
- Ability to develop intuitive models on the economics of data center workloads including data center total cost of operation and token revenues
- 2+ years direct experience in developing or deploying large scale GPU based AI applications, like LLMs, for training and inference
- Ability to quickly develop intuitive, first-principles based models of Generative AI workload performance using GPU and system architecture (FLOPS, bandwidths, etc.)
Responsibilities
- Guide the architecture of the next-generation of GPUs through an intuitive and comprehensive grasp of how GPU architecture affects performance for datacenter applications, especially Large Language Models (LLMs)
- Drive the discovery of opportunities for innovation in GPU, system, and data-center architecture by analyzing the latest data center workload trends, Deep Learning (DL) research, analyst reports, competitive landscape, and token economics
- Find opportunities where we uniquely can address customer needs, and translate these into compelling GPU value proposition and product proposals
- Distill sophisticated analyses into clear recommendations for both technical and non-technical audiences
Other
- 5+ years of total experience in technology with previous product management, AI related engineering, design or development experience highly valued
- BS or MS or equivalent experience in engineering, computer science, or another technical field. MBA a plus.
- Strong desire to learn, motivated to tackle complex problems and the ability to make sophisticated trade-offs
- Track record of managing multiple parallel efforts, collaborating with diverse teams, including performance engineers, hardware architects, and product managers
- Demonstrated ability to fully contribute to above areas within 3 months
