NVIDIA is seeking a CUDA Math Libraries Intern for 2026 to contribute to the development of accelerated mathematical and data processing libraries. You'll join a team building foundational tools like cuSparse, cuBLAS, cuSOLVER, cuFFT, and DALI that power high-performance computing and deep learning stacks.
What You'll Do
- Collaborate with team members to understand software use cases and requirements.
- Analyze the performance of GPU or CPU implementations and find opportunities for improvements.
- Prototype and develop algorithms for single node and multi GPU clusters.
- Implement new algorithms, define APIs, analyze performance, and find solutions for difficult numerical corner cases.
- Work on extending capabilities of existing libraries and building new libraries for AI and HPC applications.
What We're Looking For
- Studying towards a MS or PhD degree in Computational Science, Computer Science, Applied Mathematics, Engineering, or a related field.
- Programming skills in C/C++ and Python.
- Parallel or GPU programming experience such as AVX, NEON, OpenMP, MPI, SHMEM, CUDA or OpenCL.
Nice to Have
- Exposure to floating-point arithmetic and numerical error analysis.
- Knowledge of GPU/CPU and network hardware architecture.
- Understanding of composability and fusions, compilers, and implementation of programming languages.
- Experience implementing sparse or dense linear algebra algorithms.
- Experience with domain-specific language design and compiler optimizations, in particular sparse compilers like MLIR or TACO.
Technical Stack
- C/C++, Python
- CUDA, OpenCL
- OpenMP, MPI, SHMEM
- AVX, NEON
- MLIR, TACO
Team & Environment
Part of a team developing accelerated math and data processing libraries.
Work Mode
This position follows a hybrid work model.
NVIDIA is an equal opportunity employer.
