Senior Math Libraries Engineer - Sparse Linear Algebra

NVIDIA•Santa Clara, CA

10d•$152,000 - $287,500

About The Position

NVIDIA is seeking software engineers to develop sparse linear algebra kernels for high-performance GPU libraries such as cuSPARSE and cuDSS. These libraries support applications in AI, data analytics, scientific and engineering simulations, computer-aided engineering, electronic design automation, quantum chemistry, autonomous vehicles, and large language models. In this role, you will collaborate with other developers to design, develop, and optimize kernels for algorithms including sparse BLAS operations, direct and iterative sparse solvers, preconditioners, and algebraic multigrid. The role is suited to engineers with accelerated-computing kernel development experience and an interest in advancing accelerated computing.

Requirements

At least 5 years of overall experience developing, debugging, and optimizing high-performance sparse linear algebra software using C++ and parallel programming.
Strong fundamentals in floating-point arithmetic and implementation of sparse linear algebra primitives such as matrix-vector and matrix-matrix products.
Experience developing, maintaining, and testing sparse linear algebra libraries.
Strong collaboration, communication, and documentation habits.

Nice To Haves

PhD or MSc degree, or equivalent experience, in Computational Science and Engineering, Computer Science, Applied Mathematics, or a related science or engineering field.
Experience with parallel programming technologies such as CUDA, MPI, OpenMP, OpenACC, pthreads, or equivalent.
Good knowledge of CPU and/or GPU hardware architecture and low-level GPU performance optimization.
Familiarity with multi-frontal factorizations, iterative solvers, preconditioners, and algebraic multigrid.
Experience adopting and advancing software development practices such as CI/CD systems and project management tools such as JIRA.
Understanding of large-scale computing technologies such as PDE solvers, eigenvalue solvers, and time-domain simulation methods (e.g., CFD, FEA).
Work experience in a globally distributed and agile organization.

Responsibilities

Design, implement, and optimize scalable, high-performance numerical sparse linear algebra software for existing and future GPU architectures.
Work with library engineers, QA engineers, and interns on topics ranging from sparse BLAS operations to advanced direct and iterative sparse solvers.
Work with product management and internal and external partners to understand feature and performance requirements and contribute to library technical roadmaps.
Identify and implement opportunities to improve library quality, performance, and maintainability through re-architecting and innovative software development practices.