The role
From Nvidia's own posting.
By submitting your resume, you acknowledge that your 2027 Deep Learning internship application will be processed in accordance with NVIDIA’s Applicant Privacy Policy and you agree to our Terms of Service . We’ll review resumes on an ongoing basis, and a recruiter may reach out if your experience fits one of our many internship opportunities.
NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society — from gaming to robotics, self-driving cars to life-saving healthcare, climate change to virtual worlds where we can all connect and create. Our internships offer an excellent opportunity to expand your career and get hands on experience with one of our industry leading Deep Learning teams. We’re seeking strategic, ambitious, hard-working, and creative individuals who are passionate about helping us tackle challenges no one else can solve.
Throughout the 12-week full-time internship, students will work on projects that have a measurable impact on our business. We’re looking for students pursuing a B.S., M.S., or Ph.D. degree within a relevant or related field.
Potential Internships in this field include:
Deep Learning Applications and Algorithms
Developing algorithms for deep learning, data analytics, or scientific computing to improving performance of GPU implementations
Course or internship experience related to the following areas could be required: Deep Neural Networks, Linear Algebra, Numerical Methods and/or Computer Vision, Software Design, Computer Memory (Disk, Memory, Caches), CPU and GPU Architectures, Networking, Numeric Libraries, Embedded System Design and Development, Drivers, Real-Time Software
Deep Learning Frameworks and Libraries
Building underlying frameworks and libraries to accelerate Deep Learning on GPUs
Contributing directly to software packages such as JAX, PyTorch, and TensorFlow, integrating the latest library (e.g., cuDNN) or CUDA features, performance tuning, and analysis
Optimizing core deep learning algorithms and libraries (e.g., CuDNN, CuBLAS), maintaining build, test, and distribution infrastructure for these libraries and deep learning frameworks on NVIDIA supported platforms
Course or internship experience related to the following areas could be required: Computer Architecture (CPUs, GPUs, FPGAs or other accelerators), GPU Programming Models, Performance-Oriented Parallel Programming, Optimizing for High-Performance Computing (HPC), Algorithms, Numerical Methods
What we need to see:
Must be actively enrolled in a university pursuing a B.S., M.S., or Ph.D. degree in Electrical Engineering, Computer Engineering, or a related field, for the full duration of the internship; anticipated graduation date ( month and year ) must be clearly indicated on a resume or CV to be considered.
Depending on the internship role, prior experience or knowledge requirements could include the following programming skills and technologies: C, C++, CUDA, Python, x86, ARM CPU, GPU, Linux, Direct3D, Vulkan, OpenGL, OpenCL, Spark, Perl, Bash/Shell Scripting, Container Tools (Docker/Containers, Kubernetes), Infrastructure Platforms (AWS, Azure, GCP), Data Technologies (Kafka, ELK, Cassandra, Apache Spark), React, Go
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Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 20 USD - 71 USD.

You will also be eligible for Intern benefits .
Applications are accepted on an ongoing basis.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive 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.