About this role

Design and develop a C++-based system to simplify and accelerate computing for unstructured sparsity in DL and HPC on NVIDIA GPUs
Enable the system in languages and frameworks that are more commonly used in DL, such as Python and PyTorch
Evaluate and improve the performance of the system on real-life applications
Realize opportunities to improve library quality, performance and maintainability by writing effective and well-tested code for production use
Work closely with product management and other internal and external partners to understand feature and performance requirements and contribute to technical roadmaps

BS, MS or PhD degree in Computer Science, Applied Math, or related field (or equivalent experience)
6+ years of overall experience in developing, debugging and optimizing high-performance software using C++ and parallel programming; ideally for sparse linear algebra applications and using CUDA, MPI, OpenMP, or equivalent technologies
Experience with domain-specific language design and compiler optimizations, in particular sparse compilers (MLIR or TACO)
Excellent C++, Python, and CUDA programming skills
Strong collaboration, communication, and documentation habits and ideally experience with working in a globally distributed organization

Senior Math Libraries Engineer – Sparsity in AI

Key skills