Deep Learning Engineer - Distributed Task-Based Backends

September 15

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NVIDIA

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GPU-accelerated computing • artificial intelligence • deep learning • virtual reality • gaming

10,000+

Description

• Develop extensions to popular Deep Learning frameworks, that enable easy experimentation with various parallelization strategies! • Develop compiler optimizations and parallelization heuristics to improve the performance of AI models at extreme scales • Develop tools that enable performance debugging of AI models at large scales • Study and tune Deep Learning training workloads at large scale, including important enterprise and academic models • Support enterprise customers and partners to scale novel models using our platform • Collaborate with Deep Learning software and hardware teams across NVIDIA, to drive development of future Deep Learning libraries • Contribute to the development of runtime systems that underlay the foundation of all distributed GPU computing at NVIDIA

Requirements

• BS, MS or PhD degree in Computer Science, Electrical Engineering or related field (or equivalent experience) • 5+ years of relevant industry experience or equivalent academic experience after BS • Proficient with Python and C++ programming • Strong background with parallel and distributed programming, preferably on GPUs • Hands-on development skills using Machine Learning frameworks (e.g. PyTorch, TensorFlow, Jax, MXNet, scikit-learn etc.) • Understanding of Deep Learning training in distributed contexts (multi-GPU, multi-node)

Benefits

• Eligible for equity and benefits.