Yunkai Li
Software Engineer at Kuaishou Technology
Chaoyang District, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolYunkai Li is a software engineer with eight years of experience specializing in computer vision and deep learning, currently building software at Kuaishou Technology in Beijing. He has a solid research and engineering background from roles at MEGVII and earlier positions focused on image processing and security software, blending academic rigor from an M.S. in Electrical Engineering with practical product delivery. Yunkai contributes to open-source computer vision tooling, notably improving robustness and test automation for the widely used OpenMMLab mmdetection3d indoor pipeline. He excels at making complex data-loading and transformation pipelines reliable, a skill reflected in his focus on test coverage and pipeline validation. Colleagues describe him as detail-oriented and pragmatic—comfortable moving between research prototypes and production-grade systems. Fluent in both research and production contexts, he brings a track record of turning deep learning ideas into dependable engineering outcomes.
8 years of coding experience
7 years of employment as a software developer
Master, Electrical Engineering, 3.5 / 4.0, Master, Electrical Engineering, 3.5 / 4.0 at Arizona State UniversityBachlor, Biomedical Engineering, Bachlor, Biomedical Engineering at Northeastern University (CN)
Chinese, English
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (7)
unit-testing10
3d-object-detection10
python10
pytorch9
flash6
android6
sdk6
Programming languages (3)
Github contributions (5)
open-mmlab/mmdetection3d
May 2020 - Jul 2021
OpenMMLab's next-generation platform for general 3D object detection.
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:5 reviews, 44 commits, 26 PRs in 1 year 2 months
Contributions summary:Yunkai primarily contributed to the development and testing of an indoor pipeline within the 3D object detection framework. Their work involved creating and refining test cases to ensure the correct functionality of the indoor pipeline, including data loading, transformation, and object processing. The user's commits included changes to testing configurations and code to validate the pipeline's behavior against expected results. The user's efforts focused on enhancing the robustness and reliability of the 3D object detection system.
pytorchnext-generationkitti-datasetdeep-learningobject-detection
Megvii-BaseDetection/BEVDepth
Jul 2022 - Jan 2023
Official code for BEVDepth.
Contributions:5 releases, 10 reviews, 67 commits in 5 months
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