Senior Math Libraries Engineer – Sparse Linear Algebra

EngineerEngineerFull TimeRemoteTeam 10,001+Since 1993H1B SponsorCompany Site LinkedIn

Location

California

Posted

177 days ago

Salary

$148K - $287.5K / year

5 yrs expExperience acceptedEnglish

Job Description

• Designing, implementing and optimizing scalable high-performance numerical sparse linear algebra software for existing and future GPU architectures • Working with library engineers, QA engineers, and interns on topics ranging from sparse BLAS operations to advanced direct and iterative sparse solvers • 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

Job Requirements

PhD or MSc degree (or equivalent experience) in Computational Science and Engineering, Computer Science, Applied Mathematics, or related science or engineering field is preferred
5+ years of overall experience in developing, debugging and optimizing high-performance sparse linear algebra software using C++ and parallel programming; ideally using CUDA, MPI, OpenMP, OpenACC, pthreads, or equivalent technologies
Strong fundamentals in floating point arithmetic and implementation of sparse linear algebra primitives like matrix-vector and matrix-matrix products
Experience in developing , maintaining, and testing sparse linear algebra libraries
Strong collaboration, communication, and documentation habits.
Good knowledge of CPU and/or GPU hardware architecture and low-level GPU performance optimization
Familiarity with technologies such as multi-frontal factorizations, iterative solvers, preconditioners, and algebraic multigrid
Experience with 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)
Working experience in a globally distributed and agile organization