About the job
We are looking for software engineers to contribute to the design and development of libraries and tools to simplify and accelerate computing for unstructured sparsity in DL and HPC. Around the world, leading commercial and academic organizations are revolutionizing AI, data analytics, and scientific and engineering simulations, using data centers powered by GPUs and high-performance linear algebra libraries. Applications of these technologies include LLMs, computer aided engineering, quantum chemistry, autonomous vehicles, computer vision, and countless others. Did you know our team develops the GPU accelerated libraries and SDKs that help make these possible?
Responsibilities
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
Qualifications
Minimum
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
Preferred
Strong understanding of sparse computations, in particular sparsity in AI and HPC
Good understanding of LLMs, Deep Learning methods and frameworks
Experience with low-level GPU performance optimization
Understanding of numerical linear algebra methods like direct and iterative solvers
Experience with adopting and advancing, software development practices such as CI/CD systems and project management tools such as JIRA.