Join our team as a Senior Software Engineer focusing on sparse linear algebra kernels for high-performance libraries like cuSPARSE and cuDSS. You will collaborate with developers to design, implement, and optimize scalable numerical software for GPUs, covering basic sparse BLAS operations and advanced solvers. Your role involves working closely with QA engineers, product management, and external partners to enhance library quality and performance through innovative practices. Ideal candidates have a PhD or MSc in Computational Science and Engineering, Computer Science, Applied Mathematics, or related fields, along with 5+ years of experience in developing high-performance sparse linear algebra software using C++ and parallel programming technologies such as CUDA, MPI, OpenMP, and OpenACC. Strong knowledge of GPU hardware architecture and familiarity with advanced numerical methods are essential for this role that drives cutting-edge advancements in AI, data analytics, and scientific simulations.
Skills
C++
CUDA
MPI
OpenMP
OpenACC
pthreads
CI/CD
JIRA
GPU
CPU
sparse linear algebra
matrix-vector products
matrix-matrix products
multi-frontal factorizations
iterative solvers
preconditioners
algebraic multigrid
PDE solvers
eigenvalue solvers
time-domain simulation methods
What you'll do
Designing and implementing high-performance sparse linear algebra software for GPUs
Optimizing numerical algorithms for existing and future GPU architectures
Collaborating with engineers to develop and test sparse BLAS operations and solvers
Contributing to technical roadmaps by understanding feature requirements from partners
Improving library quality, performance, and maintainability through innovative practices
What we're looking for
5+ years experience developing high-performance sparse linear algebra software using C++ and parallel programming technologies like CUDA
Strong background in floating point arithmetic and implementation of sparse linear algebra primitives
Experience in maintaining, testing, and optimizing sparse linear algebra libraries
Good knowledge of GPU hardware architecture and low-level performance optimization techniques
Familiarity with advanced numerical methods including multi-frontal factorizations, iterative solvers, preconditioners, and AMG
Understanding of large-scale computing technologies such as PDE solvers, eigenvalue solvers, and time-domain simulation methods
Experience working in a globally distributed and agile organization
Market check
Salary context
This
$152,000–$218,500
range sits above
39%
of similar postings on FindRole.
Peer median band
$152,000–$241,500
Median floor and ceiling across peers.
Typical midpoint (25–75%)
$175,500–$235,750
Middle half of comparable postings.
Based on 240 comparable postings.*
* 240 is the maximum number of comparable postings sampled.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
802 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 798 roles with salary data.