NVIDIA has been redefining computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people! Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world!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 economicsFind opportunities where we uniquely can address customer needs, and translate these into compelling GPU value proposition and product proposalsDistill sophisticated analyses into clear recommendations for both technical and non-technical audiencesWhat we need to see:5+ years of total experience in technology with previous product management, AI related engineering, design or development experience highly valuedBS 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 architectureAbility to develop intuitive models on the economics of data center workloads including data center total cost of operation and token revenuesDemonstrated ability to fully contribute to above areas within 3 monthsStrong desire to learn, motivated to tackle complex problems and the ability to make sophisticated trade-offsWays 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 inferenceAbility 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 newsTrack record of managing multiple parallel efforts, collaborating with diverse teams, including performance engineers, hardware architects, and product managersWidely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 144,000 USD - 218,500 USD for Level 3, and 168,000 USD - 258,750 USD for Level 4.You will also be eligible for equity and benefits.Applications for this job will be accepted at least until September 20, 2025.NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.