Jun Li
Algorithm Engineer at Meituan-Dianping
Haidian District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolJun Li is an Algorithm Engineer with nine years of experience building and productionizing ML and software systems across Meituan-Dianping and Microsoft. Trained with an M.S. in Computer Science from Beijing University of Posts and Telecommunications, he blends deep engineering rigor with applied NLP expertise—contributing to the CLUE Chinese Language Understanding benchmark by integrating COPA tasks and adapting BERT/RoBERTa/XLNet models. At Meituan he focuses on scalable algorithmic solutions for high-traffic products, drawing on prior SDE experience to bridge research and production. Known on GitHub for a concise mission—"Enable Humanity :)"—he combines pragmatic bug fixes and scripting finesse with model engineering to make research artifacts usable in real-world pipelines.
9 years of coding experience
4 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at Beijing University of Posts and Telecommunications
Github Skills (15)
machine-learning10
benchmarking10
benchmark10
nlp10
python10
ch10
bert10
natural-language-processing10
pre-trained-model10
datasets10
tensorflow9
bash9
transformers9
pytorch9
xnet8
Programming languages (7)
Github contributions (5)
CLUEbenchmark/CLUE
Nov 2019 - Apr 2020
中文语言理解测评基准 Chinese Language Understanding Evaluation Benchmark: datasets, baselines, pre-trained models, corpus and leaderboard
Role in this project:
ML Engineer
ML EngineerContributions:60 commits, 6 PRs, 48 pushes in 5 months
Contributions summary:Jun primarily focused on enhancing the Chinese Language Understanding Evaluation Benchmark (CLUE) repository, specifically addressing tasks related to the COPA dataset. Their work involved modifying existing classifier scripts and utilities to incorporate COPA, along with implementing relevant bash scripts for running the classifier. The user also integrated and updated various models, including Bert, Roberta, and XLNet, to work effectively with the COPA task within the CLUE benchmark, and fixed bugs.
language-understandingpre-trained-modelnlubenchmarkchinese
DukeEnglish/chendq-thesis-ZH
Jun 2019 - Aug 2019
Chinese version of Dr chen's PhD thesis. 这里是对陈丹琦的博士毕业论文的中文翻译版本。https://chendq-thesis-zh.readthedocs.io/en/latest/
Contributions:53 commits, 51 pushes, 1 branch in 1 month
nlpchinesephd-thesischenthesis
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