1 day ago
Responsibilities
- Advise research labs, researchers, and university faculty on NVIDIA technologies and architectures.
- Drive joint research projects and accelerate multimodal and physical AI workloads on multi-node GPU systems.
- Integrate NVIDIA frameworks, libraries, and core software into research projects.
- Support publications, conference presentations, technical content, and research impact.
- Deliver hands-on trainings, workshops, lectures, demonstrations, and user mentoring.
- Track emerging research trends, develop prototype solutions, and communicate product gaps to NVIDIA Engineering.
- Maintain expertise across NVIDIA GPUs, CPUs, networking, and software platforms.
Requirements
- Graduate degree in a STEM discipline or equivalent experience.
- At least 5 years of experience across the multimodal and world model lifecycle on multi-node GPU systems, including data curation, pre-training, post-training, evaluation, and efficient inference.
- Strong collaboration, communication, analytical, organizational, and relationship-building skills.
- Fluent written and spoken English and comfort working in Python.
Benefits
- Approximately 20% travel is required.
- The role uses conferencing tools and supports flexible ways of working to serve customers.
Tech Stack
NimPython
Categories
Solutions Engineering
About Nvidia
Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics, ignited the era of modern AI and is fueling the creation of the metaverse. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry.