About the job
NVIDIA has become the platform upon which every new AI-powered application is built. We are seeking a Sr. HPC Performance engineer to join our team of scientists and engineers passionate about building the next generation of scientific machine learning (ML) frameworks. Starting with digital biology, through high performance computing (HPC) and powerful ML methods, together, we will advance NVIDIA’s capacity to accelerate AI for Science and industries that depend on it.
Responsibilities
Design and implement computationally performant features for large scale, CUDA-backed ML training frameworks, using low level acceleration and scaling strategies such as kernel design, GPU porting, data structure innovations, distributed learning technologies
Optimize computational performance of wide range of business-critical ML models via accelerated hardware and software stack, as well as algorithmic improvements
Develop and maintain HPC software stack for atomistic modeling and generative machine learning in digital biology and beyond
Collaborate with multiple HPC, AI infrastructure, and research teams
Drive the testing and maintenance of the algorithms and software modules
Qualifications
Minimum
Advanced degree in a quantitative field such as Computer Science, Computational Biophysics, Computational Chemistry, Physics, Mathematics, or equivalent experience
5+ years of relevant experience
Consistent track record in performance engineering as well as software design, building and packaging and launching software products, with a focus on acceleration
Deep understanding of parallel programming in C++, Python; programming experience CUDA or OAI Triton
Fluent in modern machine learning frameworks such as PyTorch, JAX, Warp
Experience with HPC solutions to research problems for biology or chemistry, including but not limited to atomistic simulations
Recognized for technical leadership contributions, capable of self-direction, and ability to learn from and teach others
You should display strong communication skills, be organized and self-motivated, and play well with others (be an excellent teammate!)
Preferred
Contribution to major scientific AI for Science codebase with acceleration features such as new kernels
Familiarity with pioneering language and geometric models used in AI for Science applications in biology and chemistry