NVIDIA is looking for a passionate student to work in its Developer Technology (DevTech) team as an AI Developer Technology intern. The DevTech team is a global organization whose mission is to drive innovation and enhance the value of our platforms for developers. Would you enjoy researching parallel algorithms to accelerate AI workloads on advanced computer architectures? Is it rewarding to investigate, find, and eliminate system bottlenecks to achieve the best possible performance of computer hardware? Could you be thrilled about an opportunity to partner with the Developer community, working at the forefront of technology breakthroughs that contribute to the success of an industry leader like NVIDIA? If so, the we invite you to consider this role.
What you'll be doing:
- Working closely with key application developers to understand and address their current and future challenges. Developing and optimizing core parallel algorithms and data structures, providing top solutions with GPUs through reference code and direct app contributions.
- Collaborating intimately with diverse groups at NVIDIA including architecture, research, libraries, tools, and system software teams. Your insights will influence the development of next-generation architectures, software platforms, and programming models by investigating their impact on application performance and developer efficiency.
- Researching and developing innovative techniques in AI. You'll conduct comprehensive analysis and optimization to ensure the best possible performance on current and next-generation GPU architectures.
What we need to see:
- Pursuing a Master degree or PhD degree in AI computation or system optimization with a strong computational profile.
- Strong knowledge of C++, software development, programming techniques, and AI algorithms.
- Strong communication and organization skills, with a logical approach to problem solving, good time management, and task prioritization skills.
- Proficiency in a specific domain, such as multi-modal model inference or reinforcement learning for LLMs.
