1 day ago
Base Salary
$152k - $288k/yr
Responsibilities
- Ensure platform readiness for CUDA Math Libraries, including cuBLAS, cuSPARSE, cuSOLVER, cuFFT, and other libraries.
- Triage and isolate platform-specific versus library-specific defects, fix bugs, and escalate library-specific issues when appropriate.
- Define performance targets, establish performance testing, and resolve performance regressions on new platforms.
- Collaborate with CI/CD, build, and test infrastructure teams to create platform-specific qualification and validation pipelines.
Requirements
- PhD or MSc in Computational Science and Engineering, Computer Science, Applied Mathematics, or a related science or engineering field, or equivalent experience.
- At least 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 using C++ and Python.
- Strong understanding of numerical methods and computations in science, engineering, or deep learning.
- Strong communication, collaboration, and documentation skills.
- Experience with software development practices such as CI/CD systems and project management tools such as JIRA.
- Preferred experience with CUDA, GPU-accelerated computing, MPI, OpenMP, OpenACC, or pthreads.
- Familiarity with BLAS, LAPACK, FFT, and sparse solvers.
- Experience with cross-platform software development, platform bring-up across multiple architectures, or safety-critical and embedded environments such as DriveOS and ISO 26262.
- Experience using Agentic AI to improve efficiency and code quality is a preferred qualification.
Benefits
- Hybrid work arrangement.
- Eligible for equity and benefits.
- Applications will be accepted at least until October 9, 2026.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.