Senior Math Libraries Engineer - Sparse Linear Algebra

Posted 6 Days Ago
Be an Early Applicant
Hiring Remotely in Santa Clara, CA, USA
In-Office or Remote
152K-288K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Develop and optimize high-performance sparse linear algebra kernels and libraries for GPUs. Responsibilities include implementing sparse BLAS operations, direct and iterative solvers, preconditioners, and algebraic multigrid algorithms; collaborating with library, QA, product, and external teams; improving performance, quality, maintainability, and technical roadmaps; and supporting GPU architecture optimization and software development practices.
Summary Generated by Built In

We are looking for software engineers to join our development efforts in the area of sparse linear algebra kernels for high-performance libraries such as cuSPARSE and cuDSS. Around the world, leading commercial and academic organizations are revolutionizing AI, data analytics, and scientific and engineering simulations, using data centers powered by GPUs and high-performance linear algebra libraries. Applications of these technologies include computer aided engineering (CAE), electronic design automation (EDA), quantum chemistry, autonomous vehicles, LLMs, and countless others. Did you know our team develops the GPU accelerated libraries and SDKs that help make these possible?

In this role, you will work together with other developers on designing, developing, and optimizing kernels for various algorithms including basic sparse BLAS operations like matrix-vector-products and matrix-matrix-products, direct sparse solvers, iterative sparse solvers, preconditioners, and algebraic multigrid (AMG). Ideal candidates will not only have experience developing accelerated computing kernels, but also be motivated to advance the state-of-the-art in a variety of accelerated computing domains. If this sounds exciting, we would love to meet you!

What you will be doing:

  • Designing, implementing and optimizing scalable high-performance numerical sparse linear algebra software for existing and future GPU architectures

  • Working with library engineers, QA engineers, and interns on topics ranging from sparse BLAS operations to advanced direct and iterative sparse solvers

  • 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

What we need to see:

  • PhD or MSc degree (or equivalent experience) in Computational Science and Engineering, Computer Science, Applied Mathematics, or related science or engineering field is preferred

  • 5+ years of overall experience in developing, debugging and optimizing high-performance sparse linear algebra software using C++ and parallel programming; ideally using CUDA, MPI, OpenMP, OpenACC, pthreads, or equivalent technologies

  • Strong fundamentals in floating point arithmetic and implementation of sparse linear algebra primitives like matrix-vector and matrix-matrix products

  • Experience in developing , maintaining, and testing sparse linear algebra libraries

  • Strong collaboration, communication, and documentation habits.

Ways to stand out from the crowd:

  • Good knowledge of CPU and/or GPU hardware architecture and low-level GPU performance optimization

  • Familiarity with technologies such as multi-frontal factorizations, iterative solvers, preconditioners, and algebraic multigrid

  • Experience with adopting and advancing software development practices such as CI/CD systems and project management tools such as JIRA

  • Understanding of large-scale computing technologies such as PDE solvers, eigenvalue solvers and time-domain simulation methods (e.g., CFD, FEA)

  • Working experience in a globally distributed and agile organization

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing for science and engineering. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company.” We're looking to grow our company and build our teams with the smartest people in the world! Join us at the forefront of technological advancement.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and talented people in the world working for us. If you're creative, autonomous and love a challenge, we want to hear from you!

Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are:
152,000 USD – 241,500 USD for Level 3; and 184,000 USD – 287,500 USD for Level 4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until November 3, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Skills Required

  • PhD or MSc degree, or equivalent experience, in Computational Science and Engineering, Computer Science, Applied Mathematics, or a related science or engineering field
  • 5+ years of experience developing, debugging, and optimizing high-performance sparse linear algebra software
  • Experience using C++ and parallel programming
  • Strong fundamentals in floating-point arithmetic and sparse linear algebra primitives, including matrix-vector and matrix-matrix products
  • Experience developing, maintaining, and testing sparse linear algebra libraries
  • Strong collaboration, communication, and documentation skills
  • Experience with accelerated computing kernels
  • Knowledge of CPU or GPU hardware architecture and low-level GPU performance optimization
  • Familiarity with multifrontal factorizations, iterative solvers, preconditioners, and algebraic multigrid
  • Experience with CI/CD systems and project management tools such as JIRA
  • Understanding of PDE solvers, eigenvalue solvers, and time-domain simulation methods such as CFD or FEA
  • Experience working in a globally distributed and agile organization

NVIDIA Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.

  • Equity Value & Accessibility — Feedback suggests equity grants, stock options/RSUs, and a discounted ESPP materially boost total compensation and can deliver significant wealth-building when the stock performs well.
  • Healthcare Strength — Feedback suggests medical, dental, and vision plans with in-network preventive care at 100%, telehealth, and concierge-style support are comprehensive and valued.
  • Parental & Family Support — Feedback suggests up to 22 weeks fully paid parental leave, fertility coverage, and adoption/surrogacy reimbursement provide strong family-focused benefits.

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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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