Sparse Linear Algebra Libraries Intern - Summer 2027

Today, NVIDIA is tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in an inclusive, encouraging environment where everyone is inspired to do their best work. Come join the team and see how we can make a lasting impact on the world.  

We are looking for software engineering interns for our Sparse Linear Algebra team, which develops libraries like cuSPARSE that are a key part of high-performance computing and deep learning software stacks. The main purpose of these libraries is to provide the fastest computing primitives for operations like sparse matrix-vector and sparse matrix-matrix products, among many others. We see strong interest in optimization of such key functionalities from various industrial and research organizations - from gaming and machine learning to autonomous driving and chip modeling. All of these operations need to be optimized for current and future GPUs and for a wide range of unstructured sparse matrix inputs, which can also necessitate changing the underlying algorithms mathematically and using techniques like code/kernel generation. Does the idea of being at the heart of these projects and applying your knowledge to develop and optimize algorithms which make an impact around world excite you? If yes, then come and join our team!

During your internship, you will work with senior software engineers on the libraries team who will provide mentorship and guide you in developing highly optimized algorithms. Projects will involve implementing new numerical algorithms, defining APIs, analyzing performance, finding appropriate solutions for difficult numerical corner cases, and other general software engineering work.

What you'll be doing:

• Prototype and develop numerical algorithms for high-performance sparse linear algebra libraries for GPUs
• Analyze the performance of GPU implementations and find opportunities for improvements
• Collaborate with team members to understand software use cases and requirements

What we need to see:

• Pursuing MS or PhD in Computational Science, Computer Science, Applied Mathematics, or equivalent program area.
• Strong C++ programming, debugging, performance analysis, and test design skills
• Experience implementing sparse linear algebra algorithms
• Parallel programming experience using CUDA and/or OpenMP
• Experience with domain-specific language design, code/kernel generation, and compiler optimizations

Ways to stand out from the crowd:

• Exposure to numerical methods, floating-point arithmetic and numerical error analysis
• Knowledge of GPU/CPU and network hardware architecture
• A scripting language, preferably Python
• Understanding of composability and fusions, compilers, and implementation of programming languages
• Familiarity with recent CUDA developments like cuTILE and Tile++ and eExperience with sparse compilers (MLIR or TACO)

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! 

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 for this job will be accepted at least until October 17, 2026.

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.