Senior Math Libraries Engineer – AI and Hpc

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA +4 · Remote

Senior engineer to join NVIDIA's Math Libraries team, focusing on kernel generation for AI and HPC, specifically matrix operations, JITing, and fusions. The role involves designing and implementing high-performance numerical dense linear algebra software on GPUs, providing technical leadership, and collaborating with product management.

What you'd actually do

  1. Scoping, designing, and implementing high quality and performance numerical dense linear algebra software on GPUs.
  2. Owning the execution of projects involving multiple engineers and sometimes teams.
  3. Providing technical leadership and feedback to library engineers working with you on projects and sometimes mentor interns.
  4. Working closely with product management and other internal and external customers to understand feature and performance requirements and contribute to the technical roadmaps of libraries.
  5. Finding opportunities to improve library performance and reduce code maintenance overhead through re-architecting.

Skills

Required

  • PhD, Master’s, or Bachelor's degree in Computer Science, Applied Math, or related science or engineering field of study (or equivalent experience).
  • 8+ years of experience in designing, developing, testing, maintenance, and performance optimization of HPC software using C++.
  • Strong fundamentals in kernel generation and composable library design for linear algebra.
  • Leadership skills in driving software development projects.
  • Strong collaboration, communication, and documentation habits.

Nice to have

  • Experience with parallel programming, ideally using CUDA, MPI, OpenMP, OpenACC, pthreads.
  • Good understanding of Machine Learning and Deep Learning technologies as well as knowledge of GPU (preferred) or CPU hardware architecture.
  • Experience with low level programming using assembly for performance optimization and operator fusion is a huge plus.
  • Experience with agile software development practices using project management tools such as JIRA.
  • A scripting language, preferably Python.

What the JD emphasized

  • high quality and performance numerical dense linear algebra software on GPUs
  • technical leadership
  • kernel generation

Other signals

  • GPU accelerated mathematical libraries
  • kernel generation for AI and HPC
  • matrix operations, JITing and fusions