Hyunsung Lee
Software Engineer at Spellbrush
Suwon-si, Gyeonggi, Japan
Summary
🤩
Rockstar🎓
Top SchoolHyunsung Lee is a software engineer with 10 years of experience building high-performance ML and system software, currently at Spellbrush after roles at NVIDIA and the OctoAI team that NVIDIA acquired. He specializes in recommender systems and MLSys, having improved production-grade recommenders at Kakao and contributed critical fixes and new ranking functionality to well-known open-source projects like TOROS Buffalo and benfred/implicit. His background spans system-level work (LLVM/MLIR interest) and fast LLM engine development, blending low-level optimization with practical ML training improvements. Hyunsung holds both B.S. and M.S. degrees from Sungkyunkwan University, where his research applied reinforcement learning to combinatorial optimization. Colleagues value him for shipping measurable metric fixes (AUC/validation handling, SGD updates) that directly improved model reliability in production.
10 years of coding experience
5 years of employment as a software developer
Master's degree, Computer Engineering, Master's degree, Computer Engineering at 성균관대학교고등학교, 고등학교 at 한양공업고등학교
Github Skills (16)
matrix-factorization10
machine-learning10
collaborative-filtering10
recommendation-system10
c-language10
cprogramming-language10
python10
recommender-system10
numpy9
algorithms8
algorithm8
testing8
scipy7
openmp6
cython6
Programming languages (8)
Github contributions (5)
kakao/buffalo
Sep 2019 - Jan 2023
TOROS Buffalo: A fast and scalable production-ready open source project for recommender systems
Role in this project:
ML Engineer
ML EngineerContributions:10 releases, 24 reviews, 113 commits in 3 years 4 months
Contributions summary:Hyunsung primarily contributed to the codebase by fixing errors and making improvements related to the training process of the BPRMF algorithm. Their changes involved correcting the SGD update calculations within the CBPRMF::worker function, addressing jenkins errors, and ensuring that the bias term was correctly applied when calculating top-k recommendations. Furthermore, the user addressed inconsistencies in the evaluation metrics, specifically regarding AUC and validation data handling within the buffalo library.
scalablecpppythonrecommenderproduction-ready
benfred/implicit
Apr 2018 - Jan 2022
Fast Python Collaborative Filtering for Implicit Feedback Datasets
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:11 reviews, 62 commits, 93 PRs in 3 years 9 months
Contributions summary:Hyunsung implemented a new `rank_items` method for recommendation algorithms, enhancing their ability to rank specific items for a user within a larger dataset. This involved modifying existing classes like `RecommenderBase`, `MatrixFactorizationBase`, and `ItemItemRecommender`, as well as adding tests for the new functionality. They also optimized code by removing irrelevant transformations and updating dependencies. The commits indicate a focus on improving the core functionality of the recommendation models.
pythonimplicitimplicit-feedbackcollaborative-filteringrecommendation
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