Math Device Extension Libraries Intern - 2027

We are looking for software engineering interns to help us expand our catalog of Device eXtension (Dx) APIs for our math libraries. For over a decade, NVIDIA's accelerated computing platform has revolutionized HPC and AI with applications ranging from COVID-19 research to autonomous machines. Our team develops the GPU-accelerated mathematical libraries that make all of this possible. The hardware and software accelerated computing ecosystem is constantly evolving, including shifts towards hybrid backends, deep integration with high-level languages and ecosystems (such as Python, Numpy, JAX, MLIR…), and optimization at runtime for maximum flexibility and performance. Our Dx APIs allow developers to embed highly-optimized mathematical operations in their applications for these and other scenarios. You will be part of a team designing, developing, and optimizing math libraries for the future. 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, performance optimizations on the latest NVIDIA GPUs, improving libraries customer experience in employing agentic workflows and AI tools for their development and other general software engineering work. What you'll be doing: • Prototype and develop numerical algorithms for high-performance math device extension libraries: cuBLASDx, cuSolverDx, cuFFTDx, cuRANDDx, nvCOMPDx, NPPDx or libmathdx on ground breaking NVIDIA graphics processors. • Analyze the performance of GPU implementations and find opportunities for improvements • Collaborate with team members to understand software use cases and requirements • Prototype tooling and solutions to help transform math libraries’ APIs into LLM-friendly representations across popular codegen tools. What we need to see: • Pursuing a MS or PhD degree in Computer Science, Artificial Intelligence, Applied Mathematics, or related field • Strong C++ programming, debugging, performance analysis, and test building skills • Parallel or GPU programming experience using CUDA, OpenMP, OpenCL, SYCL or similar • Experience with linear algebra or FFT or data compression algorithms • Collaboration, communication, and documentation habits. Ways to stand out from the crowd : • Understanding of composability and fusions, compilers, implementation of programming languages • Knowledge of GPU and network hardware architecture • Familiarity with Python and recent CUDA developments like cuTile and CUDA Tile C++ • Prior work in AI-assisted software development, knowledge of LLMs, MCP, and building RAGs or agenting tooling • Exposure to numerical methods, floating-point arithmetic and numerical error analysis 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. Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/ Our internship pay is based on the position, your location, year in school, degree, and experience.