We are looking for software engineers to join our development efforts in the area of dense linear algebra kernels for high-performance libraries such as cuSOLVER. Around the world, leading commercial and academic organizations are revolutionizing AI, data analytics, and scientific and engineering simulations, using data centers powered by GPUs and high-performance linear algebra libraries. Applications of these technologies include computer aided engineering (CAE), electronic design automation (EDA), quantum chemistry, autonomous vehicles, LLMs, computer vision, encryption, and countless others. Did you know our team develops the GPU accelerated libraries and SDKs that help make these possible?
In this role, you will work together with other developers on designing, developing, and optimizing kernels for various algorithms including triangular factorizations, eigenvalue decompositions and singular value decompositions. Ideal candidates will not only have experience developing accelerated computing kernels, but also be motivated to advance the state-of-the-art in a variety of accelerated computing domains. If this sounds exciting, we would love to meet you!
What you will be doing:
Designing, implementing and optimizing scalable high-performance numerical dense linear algebra software on GPUs
Providing technical leadership and guidance to library engineers, QA engineers, and interns working with you on projects
Working closely with product management and other internal and external partners to understand feature and performance requirements and contribute to the technical roadmaps of libraries
Finding and realizing opportunities to improve library quality, performance and maintainability through re-architecting and establishing innovative software development practices
What we need to see:
PhD or MSc degree in Computational Science and Engineering, Computer Science, Applied Mathematics, or related science or engineering field (or equivalent experience)
5+ years of overall experience in developing, debugging and optimizing high-performance numerical linear algebra software using C++ and parallel programming; ideally using CUDA, MPI, OpenMP, OpenACC, pthreads
Strong fundamentals in numerical methods such as computational linear algebra, linear system solvers, and methods for eigenvalue, singular value, and other decompositions
Experience developing dense linear algebra libraries such as BLAS, LAPACK; and their parallel counterparts like PBLAS and SCALAPACK
Strong collaboration, communication, and documentation habits
Ways to stand out from the crowd:
Good knowledge of CPU and/or GPU hardware architecture
Experience with adopting and advancing, software development practices such as CI/CD systems and project management tools such as JIRA.
Experience with working in a globally distributed organization
Strong background of large-scale computing technologies such as PDE solvers, eigenvalue solvers and time-domain simulation methods (e.g., CFD, FEA)
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing for science and engineering. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.” We're looking to grow our company and build our teams with the smartest people in the world! Join us at the forefront of technological advancement.
#LI-Hybrid
The base salary range is 184,000 USD - 356,500 USD. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions.
You will also be eligible for equity and benefits. NVIDIA accepts applications on an ongoing basis.
NVIDIA is committed to fostering a diverse 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.
