Senior GPU Linear Algebra Engineer - Equity Eligible

NVIDIA; New York (NY); On-site; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA in New York invites software engineers to join the dense linear algebra kernel effort behind cuSOLVER. You will design, implement, and optimize GPU kernels for triangular factorizations, eigen decompositions, and SVD, collaborating with world-class experts.

Ideal candidates have a PhD or MSc, 5+ years in high-performance numerical software with C++, CUDA and parallel tools, plus strong knowledge of BLAS/LAPACK and scalable libraries. Equity and comprehensive benefits are available.

Qualifications

• PhD or MSc in Computational Science and Engineering, CS, Applied Math, 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 (CUDA, MPI, OpenMP, OpenACC, pthreads).
• Strong fundamentals in numerical methods including linear solvers and eigen/decomposition methods.
• Experience with dense linear algebra libraries like BLAS, LAPACK and parallel variants like PBLAS/SCALAPACK.

Responsibilities

• Design, implement and optimize scalable high-performance numerical dense linear algebra software on GPUs.
• Provide technical leadership to library engineers, QA engineers, and interns on projects.
• Collaborate with product management and partners to define feature and performance roadmaps.
• Improve library quality, performance and maintainability through re-architecture and best practices.

Skills

C++

CUDA

MPI

OpenMP

OpenACC

Pthreads

Numerical linear algebra

Software performance optimization

Documentation

Collaboration

Education

PhD or MSc in Computational Science and Engineering, Computer Science, Applied Mathematics, or related field

Tools

BLAS

LAPACK

PBLAS

SCALAPACK

Job description

NVIDIA in New York invites software engineers to join the dense linear algebra kernel effort behind cuSOLVER. You will design, implement, and optimize GPU kernels for triangular factorizations, eigen decompositions, and SVD, collaborating with world-class experts.

Ideal candidates have a PhD or MSc, 5+ years in high-performance numerical software with C++, CUDA and parallel tools, plus strong knowledge of BLAS/LAPACK and scalable libraries. Equity and comprehensive benefits are available.