Description
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.
Responsibilities:
- 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
Requirements:
- 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.
Nice to Have:
- 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 like JIRA, and AI agents
- Understanding of large-scale computing technologies such as PDE solvers, eigenvalue solvers, and time-domain simulation methods (e.g., CFD, FEA)
- Working experience in a globally distributed and agile organization