Nvidia Data Center GPU Performance TCO Product Analyst 

United States, California 

619197892

What you'll be doing:

NVIDIA's Accelerated Computing team is a driving force behind the explosion of Machine Learning, Artificial Intelligence and High-Performance Computing. We are looking for a highly capable individual with a consistent track record in technology and the skills for GPU product definition for Data Center. We are a small, dynamic, and motivated team that defines the next generation of products for these high growth markets.

• 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

What we need to see:

• 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.

• 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

• Demonstrated ability to fully contribute to above areas within 3 months

• Strong desire to learn, motivated to tackle complex problems and the ability to make sophisticated trade-offs

Ways to stand out from the crowd:

• 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.)

• Comfort and drive to constantly stay updated with the latest in deep learning research (academic papers) and industry news

• Track record of managing multiple parallel efforts, collaborating with diverse teams, including performance engineers, hardware architects, and product managers