Senior Math Libraries Engineer, Sparse Linear Algebra

Nvidia

Confirmed live today High trust
Remote

Quick summary

Work type
Remote
Location
Santa Clara, CA
Salary
$152,000–$241,500 / yr
Employment
Full-time
Posted
5 days ago
Freshness
Confirmed live today

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $223k
This role $197k
$137k $289k
below market most similar roles pay here above market

This role pays less than 69% of similar roles. Most pay $196,750–$248,531 — the blue band above. At the midpoint, this role pays about $197k versus about $223k for comparable roles.

Based on 240 similar postings.

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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 1792 open roles on FindRole.

Listed pay typically runs $184,000–$287,500 across 1179 roles with salary data.

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At a glance

TL;DR · Senior Math Libraries Engineer, Sparse Linear Algebra

The Senior Math Libraries Engineer - Sparse Linear Algebra joins the development team to build and optimize sparse linear algebra kernels for high-performance libraries like cuSPARSE and cuDSS. You will design, implement, and optimize scalable numerical software for various algorithms, including basic sparse BLAS operations like matrix-vector-products, direct sparse solvers, iterative sparse solvers, preconditioners, and algebraic multigrid. The role requires developing kernels for existing and future GPU architectures while collaborating with library engineers and QA teams. Key technical requirements include proficiency in C++, CUDA, MPI, OpenMP, OpenACC, and pthreads. You must possess strong fundamentals in floating point arithmetic and sparse linear algebra primitives. This work supports critical applications in computer aided engineering, electronic design automation, quantum chemistry, autonomous vehicles, and large language models.

What you'll do

  • Design, implement, and optimize scalable high-performance numerical sparse linear algebra software for GPU architectures.
  • Develop kernels for sparse BLAS operations, including matrix-vector and matrix-matrix products.
  • Build and optimize advanced direct sparse solvers, iterative sparse solvers, preconditioners, and algebraic multigrid methods.
  • Collaborate with product management to define feature requirements and contribute to technical roadmaps.
  • Improve library quality, performance, and maintainability through re-architecting and innovative software development practices.
  • Debug and optimize high-performance sparse linear algebra software using C++ and parallel programming technologies.

What we're looking for

  • PhD or MSc degree in Computational Science and Engineering, Computer Science, Applied Mathematics, or a related field (preferred).
  • 5+ years of experience developing, debugging, and optimizing high-performance sparse linear algebra software using C++.
  • Experience with parallel programming technologies such as CUDA, MPI, OpenMP, OpenACC, pthreads, or equivalent.
  • Strong fundamentals in floating point arithmetic and implementation of sparse linear algebra primitives.
  • Experience in developing, maintaining, and testing sparse linear algebra libraries.
  • Good knowledge of CPU and/or GPU hardware architecture and low-level GPU performance optimization (preferred).
  • Familiarity with multi-frontal factorizations, iterative solvers, preconditioners, and algebraic multigrid (preferred).
  • Experience with CI/CD systems, project management tools like JIRA, and large-scale computing technologies (preferred).

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