Qikai Li
Back End Developer at ByteDance
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolQikai Li is a backend developer with four years of experience, currently at ByteDance and based in Madison, WI, who combines strong Java/Spring Boot and GraphQL skills with cloud-native deployments. He drove measurable product growth at Apob.AI by integrating advanced video-generation features and AI models—helping boost registrations by 153.7% and paid conversions by 50%—while cutting API over‑fetching by 40%. He also redesigned infrastructure using RunPod and Docker to reduce failure rates from 5% to 0.1% and double inference performance, demonstrating an unusual blend of backend engineering and ML deployment know‑how. Qikai contributes to OpenMMLab’s mmpose project, where he improved benchmarking infrastructure and test reliability by refactoring configs and enabling concurrent tmux-based tests. Earlier work includes translating complex dynamic system estimation theory into production-ready Python and delivering 85% test coverage on critical backend components at RayTools.
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
sdmmc9
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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