Data Processing Developer Technology Intern

What you will be doing:

• 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.

What we need to see:

• 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.

Ways to stand out from the crowd:

• 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.

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.

Applications will be accepted until November 8, 2026 .

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