Role Overview
We’re currently seeking for an internship position in the Data Processing Developer Technology team. Would you enjoy researching parallel algorithms to accelerate AI and Data Processing workloads on advanced computer architectures? Do you find it rewarding to identify and eliminate system bottlenecks to achieve the best possible performance on pioneering 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?
Responsibilities
- In this position, you will research and develop techniques to GPU accelerate workloads in deep learning, machine learning or other AI domains.
- Work directly with other technical experts in their fields (industry and academia) to perform in-depth analysis and optimization of complex AI and HPC algorithms to ensure optimal AI solutions on modern CPU and GPU architectures.
- Publish and/or present discovered optimization techniques in developer blogs or relevant conferences to engage and educate the developer community.
- Influence the design of next-generation hardware architectures, software, and programming models in collaboration with research, hardware, system software, libraries, and tools teams at NVIDIA.
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Required Qualifications
- Currently pursuing a PhD or Master degree in Computer Science, Computer Engineering, or related computationally focused science degree.
- Programming fluency in C/C++ with a deep understanding of algorithms and software development.
- A background that includes parallel programming, e.g., CUDA, OpenACC, OpenMP, MPI, pthreads, etc.
- Effective communication and organization skills, with a logical approach to problem solving, good time management, and prioritization skills.
Preferred Skills
- Expertise in parallelization and performance optimization of Deep Learning models arising from Natural Language Processing, Computer Vision, Recommender Systems, etc.
- Excellent understanding of linear algebra.
