We are looking for software engineers to join our math libraries teams for AI and HPC kernel generation, specifically targeting emulation of math operations across different precisions. Around the world, leading commercial and academic organizations are revolutionizing AI, scientific and engineering simulations, and data analytics, using data centers powered by GPUs. Applications of these technologies are in healthcare, NLP, VR, deep learning, autonomous vehicles and countless others. Did you know our team develops the GPU accelerated math libraries that makes all of this possible? If the idea of tinkering with bits and precision formats in math operations and applying your knowledge to develop and optimize algorithms to make an impact around world excite you, come and join our team!
What you will be doing:
Scoping, designing, and implementing high quality and performance numerical dense linear algebra software on GPUs.
Providing technical leadership and feedback to library engineers working with you on projects and sometimes mentor interns.
Working closely with product management and other internal and external customers to understand feature and performance requirements and help define the technical roadmaps of libraries.
Finding opportunities to improve library performance and reduce code maintenance overhead through re-architecting.
What we need to see:
PhD or Master’s degree in Computer Science, Applied Math, or related science or engineering field of study (or equivalent experience).
5+ years of experience in designing, developing, testing, maintenance, and performance optimization of production software using CUDA and C++.
Good knowledge of GPU (preferred) or CPU hardware architecture.
Strong fundamentals in finite precision arithmetics and numerical methods for linear algebra.
Great teamwork, communication, and documentation habits.
Ways to stand out from the crowd:
Experience with CUTLASS, or low level programming like assembly for performance optimization is a huge plus.
A scripting language, preferably Python.
Experience with working in a globally-distributed team.
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, we are increasingly known as “the AI computing company”. We're looking to grow our company, and build our teams with the smartest people in the world. Join us at the forefront of technological advancement.
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to outstanding growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.
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What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
