Senior Engineer, Math Libraries - Consumer and Embedded Platforms

NVIDIA•Us, CA

33d•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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).

Responsibilities

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.