NVIDIA is looking for an expert software engineer to help us deliver CUDA-X libraries across the NVIDIA CPU and GPU ecosystem. For over a decade, NVIDIA's accelerated computing platform has revolutionized HPC and AI with applications ranging from COVID-19 research to autonomous machines. Did you know that our team develops the GPU/CPU-accelerated mathematical libraries that make all of this possible?
The hardware and software accelerated computing ecosystem is constantly evolving, including shifts towards hybrid backends, deep integration with high-level languages and ecosystems (such as Python, Numpy, JAX, MLIR…), and optimization at runtime for maximum flexibility and performance. Our libraries follow CUDA Everywhere approach to let developers use highly-optimized mathematical operations on all hardware available in NVIDIA ecosystem. You will be part of a team designing, developing, and optimizing math libraries for the future. If you are passionate about designing modern HPC libraries and want to build software that will stand the test-of-time as it accelerates countless applications, we might have the dream job you have been waiting for!
What you'll be doing:
Design modern, flexible, and easy to use APIs and kernels for math libraries and lead design reviews with all collaborators.
Work closely with internal (e.g., Engineering, Product Management) and external partners such as researchers to understand their use cases and requirements.
Work with internal and external customers to deliver timely math libraries releases.
Become a domain expert by continuously surveying current trends in software systems.
What we need to see:
PhD or MSc degree in Computer Science, Applied Math, or a related science or engineering field is preferred (or equivalent experience).
12+ years of experience designing and developing software for high-performance computing and/or AI applications.
Advanced C++ skills, including modern design paradigms (e.g., template meta-programming, RAII).
Parallel programming experience with CUDA, OpenCL or vector programming on CPU (AVX, NEON or similar).
Strong collaboration, communication, and documentation habits.
Experience with ARM, RISC-V and/or x86_64 CPU architectures.
Ways to stand out from the crowd:
Strong background in numerical methods (e.g., FFT, numerical linear algebra).
Programming skills with Python, and modern automation setups for both building software (e.g. cmake) as well as testing it (e.g. CI/CD, sanitizers).
Experience with cross-compilation, setting up CPU/GPU/accelerator (cross-)compilation toolchains and bringing existing codes to new architectures.
Background with CCCL, OpenMP, OpenACC, multi-threading, MPI, PGAS.
Experience with scientific and deep learning libraries and frameworks such as PyTorch, JAX, MKL, MAGMA, PETSc, Kokkos, etc.
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 unprecedented 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!
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 431,250 USD for Level 6.You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Top Skills
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.”
