Senior GPU Dense Linear Algebra Engineer (Equity)

NVIDIA; Washington (District of Columbia); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity compensation

Benefits

Job summary

NVIDIA is seeking software engineers to advance dense linear algebra kernels for cuSOLVER and other high-performance libraries. You will design, implement and optimize scalable numerical software on GPUs, while guiding engineers and collaborating with product teams to shape technical roadmaps.

Ideal candidates have strong numerical methods background, 5+ years in high-performance computing, and expertise with CUDA, MPI, OpenMP, OpenACC and related tools.

Qualifications

• PhD or MSc in Computational Science and Engineering, Computer Science, Applied Mathematics, or related field (or equivalent experience).
• 5+ years of experience in developing, debugging and optimizing high-performance numerical linear algebra software using C++ and parallel programming; ideally CUDA, MPI, OpenMP, OpenACC, pthreads.
• Strong fundamentals in numerical methods including linear systems, eigenvalues, singular values, and decompositions.
• Experience developing dense linear algebra libraries such as BLAS, LAPACK and their parallel counterparts like PBLAS and SCALAPACK.

Responsibilities

• Design, implement and optimize scalable high-performance numerical dense linear algebra software on GPUs.
• Provide technical leadership and guidance to library engineers, QA engineers, and interns.
• Collaborate with product management and partners to understand feature and performance requirements and contribute to roadmaps.
• Improve library quality, performance and maintainability through re-architecting and innovative software practices.

Skills

C++

Numerical linear algebra

Parallel programming

Communication

Education

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

Tools

CUDA

MPI

OpenMP

OpenACC

pthreads

Job description

NVIDIA is seeking software engineers to advance dense linear algebra kernels for cuSOLVER and other high-performance libraries. You will design, implement and optimize scalable numerical software on GPUs, while guiding engineers and collaborating with product teams to shape technical roadmaps.

Ideal candidates have strong numerical methods background, 5+ years in high-performance computing, and expertise with CUDA, MPI, OpenMP, OpenACC and related tools.