About this Engineering Manager, Math Libraries Consumer and Embedded Platforms role at NVIDIA
We are looking for a Software Engineering Manager to lead a team responsible for platform expansion & readiness, 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 the world’s AI infrastructure and must deliver functionality and performance on every target.
In this role, you will lead and build a team that provides a single accountable organization for platform integration, functional qualification, compliance, and performance readiness across CUDA Math Libraries, working closely with the core library and devops engineering teams to ensure consistent, high-quality support on every expanding platform. Ideal candidates will be hands-on engineers who also have experience leading software product engineering teams in accelerated computing domains. If this sounds exciting, we would love to meet you!
What You’ll Be Doing:
- Lead, mentor, and develop your team.
- Own end-to-end platform readiness across Math Libraries for specialized platforms including integration, functional qualification, and compliance.
- Collaborate with CI/CD, build, and test infrastructure teams to establish platform-specific qualification and validation pipelines.
- 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.
- Define and deliver to a technical roadmap for platform readiness that scales with the number and complexity of supported platforms.
- Work closely within a team of product, engineering, and program managers for dependency coordination across library teams, CUDA, compilers, QA, release processing, and platform organizations.
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).
- 8+ years of overall experience developing high-performance numerical software.
- 3+ years leading and mentoring software engineering teams.
- Hands-on 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, and motivation to adopt and advance, 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.
- Experience 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 224,000 USD - 356,500 USD for Level 3, and 272,000 USD - 431,250 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.
