NVIDIAImage and Data Processing Libraries Intern
AI in this role
Develop and optimize GPU-accelerated image and data processing libraries for AI and HPC applications as an intern at NVIDIA.
Around the world, leading commercial and academic organizations are using GPU to redefine AI, scientific and engineering simulations, and data analytics. Did you know that the GPU accelerated libraries developed by NVIDIA are used in such industries such as healthcare, VR, autonomous vehicles, social media, and e-commerce? If the idea of contributing to these excite you, come join our team and help us build accelerated software libraries which are used by the entire world.
In this role you will be part of our team responsible for the development of libraries that provide groundbreaking functionality and performance. The internship may include extending the capabilities of existing as well as building new libraries that will be used in various AI and HPC applications. It will involve working with senior software engineers who will provide mentorship and guidance.
The project will include implementing new image and data processing algorithms, defining APIs, analyzing performance, finding appropriate solutions for difficult numerical corner cases, and other general software engineering work.
What you’ll be doing:
- Collaborating with team members and other partners to understand software use cases and requirements.
- Research, analyze, and document state-of-the art algorithms
- Develop algorithms for image and data compression and processing
- Analyze and improve performance of existing implementations
What we need to see:
- Pursuing a degree in Computer Science, Artificial Intelligence, Applied Math, or related field
- Programming skills (C/C++, Python)
- Parallel or GPU programming experience (AVX, NEON, OpenMP, MPI, CUDA or OpenCL)
- Experience with image coding (jpg, tiff, etc) or data compression algorithms (LZW, DEFLATE, etc)
Ways to stand out from the crowd:
- Knowledge of image processing algorithms
- Performance analysis and test design skills
- Familiarity with floating-point arithmetic internals and numerical error analysis.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
How we rate this
Image and Data Processing Libraries Intern at NVIDIA rates 60 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
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Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where image processing was part of your work. What did you do?
- Tell me about a project where data compression was part of your work. What did you do?
- Tell me about a project where algorithm development was part of your work. What did you do?
- Tell me about a project where gpu programming was part of your work. What did you do?
- Tell me about a project where performance analysis was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Image Processing, Data Compression, Algorithm Development, GPU Programming, and Performance Analysis. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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