Youngeun Kwon
Senior Deep Learning Performance Engineer at NVIDIA
California, United States
Summary
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Rockstar🎓
Top SchoolYoungeun Kwon is a Senior Deep Learning Performance Engineer based in California, currently focusing on LLM training performance at NVIDIA. With a PhD in Electrical Engineering from KAIST and research roots in VIA Research Group, she bridges academic rigor and production-scale model optimization. Her background includes hands-on research assistant roles at KAIST and POSTECH, where she developed deep expertise in accelerator-aware ML systems. Though early in industry tenure, she brings a researcher's depth to performance engineering, especially around pre- and post-training bottlenecks for large models. Beyond engineering, she maintains a public academic profile and a personal website, signaling active engagement with the research community and reproducible work.
1 year of coding experience
7 years of employment as a software developer
Bachelor's degree, Computer Science and Engineering, Bachelor's degree, Computer Science and Engineering at Pohang University of Science and TechnologyBachelor of Engineering - BE, Computer Science and Engineering, Bachelor of Engineering - BE, Computer Science and Engineering at 포항공과대학교Doctor of Philosophy - PhD, EE, Doctor of Philosophy - PhD, EE at Korea Advanced Institute of Science and Technology
English, Korean
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (45)
speaker-diarization10
speech-to-text10
pytorch10
machine-translation10
python10
machine-learning10
speech-synthesis10
multimodal10
text-normalization10
large-language-models10
nmt10
transformer-models10
nvidia10
deep-learning10
gpu10
Programming languages (1)
Github contributions (5)
youngeunkwon0405/NeMo
Sep 2024 - Feb 2025
A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
Contributions:49 pushes, 9 branches in 5 months
youngeunkwon0405/TransformerEngine
Oct 2024 - Mar 2025
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.
Contributions:54 pushes, 10 branches in 5 months
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