Senior GPU Dense Linear Algebra Kernel Engineer (Equity)

NVIDIA; Washington; On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA’s team develops GPU-accelerated libraries and SDKs for dense linear algebra, enabling high-performance software across AI, analytics, and simulations. You will design, implement, and optimize kernel routines for triangular factorizations, eigenvalue/singular value decompositions, and related algorithms on GPUs.

We seek candidates with a PhD or MSc in computational science or related fields, 5+ years in high-performance numerical software using C++ and CUDA.

Qualifications

• 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

Responsibilities

• 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

Skills

C++ programming

Parallel programming

Numerical linear algebra

Communication skills

Documentation habits

Education

PhD or MSc in Computational Science and Engineering / CS / Applied Math

Tools

CUDA

MPI

OpenMP

OpenACC

pthreads

Job description

NVIDIA’s team develops GPU-accelerated libraries and SDKs for dense linear algebra, enabling high-performance software across AI, analytics, and simulations. You will design, implement, and optimize kernel routines for triangular factorizations, eigenvalue/singular value decompositions, and related algorithms on GPUs.

We seek candidates with a PhD or MSc in computational science or related fields, 5+ years in high-performance numerical software using C++ and CUDA.