Jin Ahn
Machine Learning Engineer at Qualcomm
Seoul, South Korea
Summary
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Rockstar🎓
Top SchoolJin Ahn is a machine learning engineer with 8 years of experience specializing in efficient large language model training and inference, currently working at Qualcomm after leading LLM R&D at Samsung Electronics. His background spans academic research at the University of Freiburg and applied deep learning for defense-related object detection, giving him a strong mix of theoretical and production-focused skills. He contributes to open-source AutoML tooling—improving metrics, ensembles, and test coverage in the widely used auto-sklearn project—reflecting attention to reliability and evaluation. Comfortable across research and engineering boundaries, he designs practical solutions for model efficiency and robustness at scale. Based in Seoul, he combines a Master’s in Computer Science (AI) with cross-disciplinary roots in life sciences, which informs a curious, system-level approach to ML problems.
8 years of coding experience
6 years of employment as a software developer
Master's degree Computer Science with specialization in Artificial Intelligence, Master's degree Computer Science with specialization in Artificial Intelligence at The University of Freiburg
English, German, Korean, Chinese
Github Skills (9)
scikit10
unit-testing10
ensembles10
automated-machine-learning10
automl10
python10
documentation10
scikit-learn10
machine-learning9
Programming languages (2)
Github contributions (5)
automl/auto-sklearn
Mar 2018 - Aug 2019
Automated Machine Learning with scikit-learn
Role in this project:
Back-end Developer
Back-end DeveloperContributions:154 commits, 53 PRs, 709 pushes in 1 year 5 months
Contributions summary:Jin primarily contributed to the documentation and unit testing of the auto-sklearn library's metrics and ensemble building functionalities. Their work included adding documentation for the `sprint_statistics` method and the averaging mechanisms used by the built-in metrics. They also added and modified unit tests for metric functions, demonstrating a focus on ensuring the accuracy and reliability of the library's core components. Furthermore, the user addressed errors in the codebase, like the wrong results of ensemble models when directly called and the error in removing the trajectory.
pythonmeta-learningmetalearningbayesian-optimizationhyperparameter-tuning
ahn1340/AutoDL
Jan 2020 - Jan 2021
AutoDL project as part of Master Project in University Freiburg, 2019/2020
Contributions:11 commits, 1 PR, 14 pushes in 1 year
javaautodlfreiburgmaster
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