Nvidia Software Engineering Manager Sparse Linear Algebra Libraries 

United States, Texas 

430363092

What you will be doing:

• Lead, mentor, and grow your library engineering team.

• Be responsible for the quality and performance of your libraries and the planning and execution of projects.

• Work closely with NVIDIA Research, Developer Technology, and Product Management teams in the areas of scientific computing, programming systems, and AI to help collect requirements for your products as well as contribute to the development of technology roadmaps.

• Interact with external partners and researchers to understand their use cases and requirements.

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 in developing high-performance numerical software.

• 3+ years of experience recruiting, training and leading software engineering teams.

• Strong fundamentals in numerical methods such as computational sparse/dense linear algebra and direct and iterative solvers for linear systems.

• Hands-on experience with user facing API design, object-oriented programming, large system software architecture development, testing, maintenance, and performance optimization of HPC software using C++ and Python.

• Experience with parallel programming, ideally using CUDA, MPI, OpenMP, OpenACC, pthreads.

• Strong collaboration, communication, 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 working in a globally distributed organization.

• Good knowledge of CPU and/or GPU hardware architecture.

• Good understanding of Machine Learning and Deep Learning technologies and experience using one or more deep learning frameworks (e.g., PyTorch, JAX, TensorFlow...).

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

• Experience with leading and mentoring teams in dynamic environments such as concurrent HW and SW development.