Senior HPC Performance Engineer - AI for Science at Scale

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

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!)