Senior Math Libraries Engineer - Direct Sparse Solvers

About the role

We are looking for software engineers to join our development efforts in cuDSS, a CUDA library for direct solvers for sparse linear systems. Around the world, leading commercial and academic organizations are revolutionizing scientific and engineering simulations, data analytics, and AI 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), optimization, quantum chemistry, autonomous vehicles, LLMs, 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 in crafting algorithms and kernels for direct sparse solvers. Ideal candidates will not only have experience developing and optimizing accelerated computing kernels, but also demonstrate dedication to advancing 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 direct sparse solvers for existing and future GPU architectures

- Working with library engineers, QA engineers, and interns on all library development aspects from design to implementation and test to release and support

- 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 for sparse linear algebra libraries through re-architecting and establishing innovative software development practices

What we need to see:

- PhD or MSc degree in Computer Science, Computational Science and Engineering, Applied Mathematics, or related science or engineering field (or equivalent experience)

- 5+ years of overall experience developing, debugging, and optimizing high-performance numerical software using C++ and parallel programming; ideally using CUDA, MPI, OpenMP, OpenACC, pthreads, or equivalent technologies

- Strong fundamentals in floating-point arithmetic, numerical analysis, and implementation of sparse linear algebra primitives like matrix-vector and matrix-matrix products and triangular solves

- Experience in developing, maintaining, and testing scientific computing libraries

- Strong collaboration, communication, and documentation habits.

Ways to stand out from the crowd:

- Familiarity with techniques in direct solvers such as reordering, multi-frontal factorizations, supernodal factorizations, numerical pivoting strategies, and iterative refinement

- Good knowledge of CPU and/or GPU hardware architecture and low-level GPU performance optimization

- Experience with adopting and advancing modern methods in software engineering such as CI/CD systems, project management tools such as JIRA, and AI agents