Top expert inAndroid Development and Reverse Engineering
Qi Jiang is a Machine Learning Research Engineer with 13 years of software and ML experience, currently focused on Language AI at Apple after building search and multimodal ranking systems at TikTok. Trained at Carnegie Mellon (MS Intelligent Information Systems) and NTU, Qi blends academic research—reinforcement learning for conversational tutoring—with hands-on production engineering across Android, full-stack web, and ML systems. He has a strong Android pedigree, contributing stabilizing fixes to Bytedance’s popular scene framework and refactoring a Sina Weibo client, showing attention to UI/UX and lifecycle edge cases. At Meta he shipped a self-service contract metadata tool that reduced on-call load by 25%, and his research work powered an OPE bot used by 200+ students. Comfortable across Python, Java, React, GraphQL and TensorFlow, Qi combines product-minded engineering with research rigor to deploy ML features that measurably improve user and developer workflows. Fluent in cross-team collaboration and rapid prototyping, he often surfaces practical engineering improvements that make complex systems more robust.
13 years of coding experience
3 years of employment as a software developer
Exchange Student Electrical and Electronics Engineering, Exchange Student Electrical and Electronics Engineering at University of Pittsburgh
Master of Science - MS Intelligent Information Systems, Master of Science - MS Intelligent Information Systems at Carnegie Mellon University
Bachelor of Engineering - BE Electrical and Electronic Engineering, Bachelor of Engineering - BE Electrical and Electronic Engineering at Nanyang Technological University Singapore
Contributions:3132 commits, 6 pushes, 1 comment in 3 years 3 months
Contributions summary:Qi's contributions primarily involved refactoring the codebase of a Sina Weibo Android client, specifically targeting improvements within UI and functionality. Code changes focused on updating existing code to better interact with backend services and databases. Refactoring work was performed across several core modules within the application related to display and data retrieval.
Android Single Activity Applications Framework compatible with Fragment.
Role in this project:
Mobile Developer
Contributions:11 releases, 325 commits, 3 PRs in 3 years 5 months
Contributions summary:Qi primarily contributed to the `bytedance/scene` repository, which is an Android Single Activity Applications Framework compatible with Fragments. The commits focus on stabilizing and improving the framework's core functionality. The user fixed issues related to UI thread operations within the navigation scene and also addressed bugs in the handling of translucent scenes, the correct display order in various cases, and the recovery of the activity status.
navigationnavigatormodularityfragmentandroid
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Qi Jiang - Machine Learning Research Engineer at Apple