Nvidia Senior Scientific Machine Learning Software Engineer - Physics 

United States, Texas 

158103243

What you'll be doing:

• Work with some of the brightest minds in a leading AI company to develop a leading machine learning framework, NVIDIA Modulus, for our academic and industrial partners to construct digital twins and machine learning simulation surrogates for real world science and engineering problems

• Work with internal project teams to validate applications built using the framework on Nvidia’s products

• Stay up to date with the latest research and innovations in deep learning techniques, implement and experiment with new insights to develop and enhance NVIDIA's deep learning technologies with focus on simulations

What we need to see:

• BS or MS degree (PhD preferred) in computer science, mathematics, computational science/engineering, or related technical field or equivalent experience.

• 10+ years of relevant experience.

• Strong Python programming skills. Familiarity with containers, numeric libraries, modular software design

• Good knowledge of state-of-the-art DNN architectures and machine learning techniques and algorithms (graph networks, diffusion models, reinforcement learning etc.) with experience in developing or using major deep learning frameworks (PyTorch, Tensorflow, JAX etc.)

• Experience with solving and using machine learning for real world problems involvingscientific/engineeringsimulations(domains/applications- industrial, life sciences, high energy physics, earth sciences – seismic, weather & climate modeling; physics types - CFD, structural, electromagnetics, optics, acoustics etc.) and/or scientific visualization is a big plus

• Strong analytical skills with bias for action. Good time-management and organization skills to thrive in a fast paced, dynamic environment

• Solid written and oral communications skills. Good teamwork and interpersonal skills

Ways to stand out from the crowd:

• Work with multi-node systems with data-parallel and model parallel programming experience. Experience with CUDA

• Usage of nonlinear simulation tools and techniques, usage of major simulation codes (opensource and/or commercial)

• Published papers in the field of AI in scientific computing