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Nvidia Senior HPC Performance Engineer - AI Science Scale 
United States, Texas 
660500394

Today
US, CA, Santa Clara
US, NC, Remote
US, TX, Remote
US, OR, Remote
US, MA, Remote
time type
Full time
posted on
Posted Yesterday
job requisition id

What you'll be doing:

  • Design and implement computationally performant features for large scale, CUDA-backed ML training frameworks, using low level acceleration and scaling strategies such as 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 generative machine learning models in digital biology and beyond

  • Collaborate with multiple HPC, AI infrastructure, and research teams

  • Develop tools to assist data processing, data quality control, algorithm development, and algorithm testing

  • Drive the testing and maintenance of the algorithms and software modules

What we need to see:

  • Advanced degree in a quantitative field such as Computer Science, Computational Biophysics, Computational Chemistry, Physics, Mathematics, or equivalent experience

  • 8+ years of relevant experience

  • Proven track record in performance engineering as well as software design, building and packaging and launching software products

  • Deep understanding of parallel programming in C++, Python; ideally CUDA programming experience

  • Proficient in modern machine learning frameworks such as PyTorch, TensorFlow, JAX, Warp

  • Experience with HPC solutions to research problems, ideally for biology, chemistry or material science applications

  • 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!)

Ways to stand out from the crowd:

  • Contributor to major scientific AI for Science codebase

  • Familiarity with pioneering language and geometric models used in AI for Science applications in biology, chemistry, material science

You will also be eligible for equity and .