Sejoon Oh
GenAI ML Research Scientist at Netflix
California, United States
Summary
👤
Senior🎓
Top SchoolSejoon Oh is a GenAI ML Research Scientist at Netflix with nine years of experience bridging academic rigor and product-focused AI research. He holds a PhD from Georgia Tech and has a strong publication record in recommender systems, adversarial ML, and generative models developed during graduate research and multiple industry internships. At Netflix he works on foundation models for personalization, intent prediction, and knowledge transfer via distillation, building systems that align large language models with user taste. His background includes scalable tensor factorization and high-performance data mining from earlier projects, bringing a systems-aware perspective to model design and deployment. Sejoon’s blend of academic depth, production internships at industry leaders (Adobe, Home Depot, Watcha), and even prior service as a KATUSA translator highlights both technical versatility and cross-cultural communication skills. He is known for translating complex research into practical recommender and personalization features that measurably improve user intent prediction.
9 years of coding experience
6 years of employment as a software developer
Bachelor of Science - BS Computer Science & Engineering, Bachelor of Science - BS Computer Science & Engineering at Seoul National UniversityDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Georgia Institute of TechnologyHansung Science High SchoolDoctor of Philosophy - PhD Computational Biology, Doctor of Philosophy - PhD Computational Biology at Carnegie Mellon University
Github Skills (14)
heterogeneous8
autoencoder8
factorization8
sparse7
manifold6
python5
high-performance5
machine-learning5
scalable4
gradient-descent3
automatic-differentiation3
linear-algebra3
modeling2
programming-language1
Programming languages (2)
Github contributions (5)
sejoonoh/P-Tucker
May 2018 - May 2020
Scalable Tucker Factorization for Sparse Tensors - Algorithms and Discoveries
Contributions:8 commits, 1 PR, 11 pushes in 2 years
scalableautoencoderpythonlinear-algebrafactorization
sejoonoh/GTA-Tensor
Jul 2018 - May 2020
High-Performance Tucker Factorization on Heterogeneous Platforms (GTA) - TPDS 2019
Contributions:23 commits, 1 PR, 21 pushes in 1 year 10 months
heterogeneousfactorizationplatformsmachine-learninggta
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