Zekun Li
Software Engineer at Bloomberg
Santa Barbara, California, United States
Summary
🤩
Rockstar🎓
Top SchoolZekun Li is a software engineer with nine years of experience building backend systems and data-driven features, currently contributing at Bloomberg after internships and engineering roles at TikTok, ByteDance, and Tsinghua University. He holds a near-perfect academic record with an MS in Electrical and Computer Engineering from Carnegie Mellon and a BE from The University of Manchester, reflecting strong theoretical grounding alongside practical delivery. Proficient in Go, C/C++, Java, and Python, he has specialized on ads and intelligent marketing systems, bringing both low-level systems thinking and product-oriented data engineering to production services. His background spans industry and research—having been a research intern in the UK and listed as a PhD student interested in NLP/AI on GitHub—indicating an appetite for applied machine learning work. Based in Santa Barbara, he combines rigorous academic training with large-scale engineering experience and a track record of shipping impactful features in high-throughput environments. Colleagues would describe him as a technically versatile engineer who bridges research insights and production-grade system design.
9 years of coding experience
1 year of employment as a software developer
Bachelor of Engineering - BE, Electronic Engineering, 4.0/4.0, Bachelor of Engineering - BE, Electronic Engineering, 4.0/4.0 at The University of ManchesterMaster of Science - MS, Electrical and Computer Engineering, 3.92/4.0, Master of Science - MS, Electrical and Computer Engineering, 3.92/4.0 at Carnegie Mellon University
English, Chinese, Japanese, Chinese
Github Skills (67)
conversational-ai9
cli9
gnn9
prediction-model9
dialogue-systems9
utterances9
compression9
discord9
multi-agent9
beam-search9
seq2seq9
terminal-ui9
sampling8
python8
deep-learning8
Programming languages (4)
Github contributions (5)
CRIPAC-DIG/GraphCTR
Jan 2022 - Dec 2022
This repo includes some graph-based CTR prediction models and other representative baselines.
Contributions:6 commits, 4 pushes, 1 branch in 11 months
graphprediction-modelfactorization-machinesfeature-interactionsgraph-factorization
Leezekun/dialogic
Aug 2022 - Oct 2022
[EMNLP 2022] Code and data for "Controllable Dialogue Simulation with In-Context Learning"
Contributions:32 commits, 12 pushes in 2 months
simulation
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