Nvidia Deep Learning Engineer - Distributed Task-Based Backends 

United States, Texas 

792084289

What You Will Be Doing:

• 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

What We Need To See:

• 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)

Ways To Stand Out From The Crowd:

• Experience with deep-learning compiler stacks such as XLA, MLIR, Torch Dynamo

• Background in performance analysis, profiling and tuning of HPC/AI workloads

• Experience with CUDA programming and GPU performance optimization

• Background with tasking or asynchronous runtimes, especially data-centric initiatives such as Legion

• Experience building, debugging, profiling and optimizing multi-node applications, on supercomputers or the cloud