We are looking for a Senior Software Engineer to join our team enabling CUDA Math Libraries on new and specialized platforms. Around the world, leading commercial and academic organizations are revolutionizing AI, data analytics, and scientific and engineering simulations using data centers powered by GPUs. NVIDIA’s math libraries are core to global AI infrastructure and must deliver functionality and performance on every target.
In this role, you will enable NVIDIA’s CUDA Math Libraries on a wide variety of emerging platforms, including integration, functional qualification, compliance, and performance readiness, working closely with the core library and devops engineering teams to ensure consistent, high-quality support on every expanding platform. Ideal candidates will have experience in scientific and numerical computing libraries, GPU kernel development, and parallel programming. This is your opportunity to join an organization that is on the forefront of unlocking application performance in the AI era! We would love for you to bring your talent to the team.
What You’ll Be Doing:
- Ensure platform readiness across Math Libraries (including cuBLAS, cuSPARSE, cuSOLVER, cuFFT, and others) for new and specialized platforms.
- Perform defect triage and isolation of platform-specific versus library-specific problems, fixing bugs to ensure functional correctness and referring to a library specialist as needed.
- Identify performance targets for new platforms, establish performance testing and fix performance regressions.
- Collaborate with CI/CD, build, and test infrastructure teams to establish platform-specific qualification and validation pipelines.
What We Need to See:
- PhD or MSc degree in Computational Science and Engineering, Computer Science, Applied Mathematics, or related science or engineering field (or equivalent experience).
- 5+ years of overall experience in developing high-performance numerical software.
- Experience with object-oriented programming, large system software architecture development, parallel computing, testing, maintenance, and performance optimization of HPC software using C++ and Python.
- Strong understanding of fundamental numerical methods and computations in science, engineering, and/or deep learning.
- Strong communication, collaboration, and documentation habits.
- Experience with software development practices, such as CI/CD systems, and project management tools such as JIRA.
Ways to Stand Out from the Crowd:
- Experience with CUDA, GPU-accelerated computing, and parallel programming (e.g. MPI, OpenMP, OpenACC, pthreads).
- Familiarity with math libraries (BLAS, LAPACK, FFT, sparse solvers).
- Proven track record using Agentic AI to boost your efficiency and code quality.
- Experience with cross-platform software development and platform bring-up across multiple architectures.
- Background with delivering software for safety-critical or embedded environments (e.g., DriveOS, ISO 26262).
#LI-Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 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.
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 in Computational Science and Engineering, Computer Science, Applied Mathematics, or a related science or engineering field, or equivalent experience
- 5+ years of experience developing high-performance numerical software
- Experience with object-oriented programming, large-system software architecture, parallel computing, testing, maintenance, and performance optimization of HPC software
- Proficiency with C++ and Python
- Strong understanding of numerical methods and computations in science, engineering, or deep learning
- Strong communication, collaboration, and documentation skills
- Experience with CI/CD systems and project management tools such as JIRA
- Experience with CUDA, GPU-accelerated computing, and parallel programming such as MPI, OpenMP, OpenACC, or pthreads
- Familiarity with BLAS, LAPACK, FFT, and sparse solver libraries
- Experience using Agentic AI to improve efficiency and code quality
- Experience with cross-platform software development and platform bring-up across multiple architectures
- Experience delivering software for safety-critical or embedded environments, such as DriveOS or ISO 26262
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.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
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.”
