Qikai Li
Back End Developer at ByteDance
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolQikai Li is a backend developer with four years of experience building high-performance, cloud-deployed services using Java, Spring Boot, GraphQL and AWS. He has a proven track record of driving user and revenue growth through engineering—implementations at Apob.AI contributed to a 153.7% uplift in registrations and 300% revenue growth while halving inference latency and nearly eliminating infrastructure failures. His work spans API design, MySQL schema optimization, and containerized deployment, and he has applied ML engineering skills to improve benchmarking and testing in the widely used open-mmlab/mmpose project. Comfortable translating research into production, he previously turned academic causation-entropy research into reusable Python tooling and has delivered high test coverage and reliable CNC machine control services in industry roles. Based in Shanghai and educated at the University of Wisconsin–Madison, he’s seeking backend challenges that combine systems performance, AI integration, and scalable architecture.
4 years of coding experience
2 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at University of Wisconsin-MadisonIB Diploma, 41/45, IB Diploma, 41/45 at Shanghai Pinghe School
Github Skills (10)
pytorch10
benchmarking10
benchmark10
tmux10
python10
pose-estimation10
testing10
yaml9
sd-mmc9
yml-configuration9
Programming languages (3)
Github contributions (5)
open-mmlab/mmpose
Aug 2021 - Dec 2022
OpenMMLab Pose Estimation Toolbox and Benchmark.
Role in this project:
ML Engineer
ML EngineerContributions:162 reviews, 74 commits, 88 PRs in 1 year 4 months
Contributions summary:Qikai's contributions focused on developing and testing benchmark regression scripts within the context of a pose estimation toolbox. This involved refactoring configuration files, modifying scripts, and integrating the use of `tmux` for running multiple benchmark tests concurrently. The changes indicate an effort to improve the testing infrastructure and ensure the reliability and performance of the pose estimation models. These enhancements are critical for evaluating model performance and verifying that the software functions as expected across different configurations.
hourglassface-keypointbenchmarkopenmmlabhuman-pose
liqikai9/mmpose
Jul 2021 - Dec 2022
OpenMMLab Pose Estimation Toolbox and Benchmark.
Contributions:2 PRs, 274 pushes, 179 branches in 1 year 4 months
pytorchdeep-learningtoolboxpose-estimationbenchmark
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