Junjie Ke
Staff Software Engineer at Google DeepMind
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolJunjie Ke is a Staff Software Engineer at Google DeepMind, focused on computer vision, machine learning, and advancing generative and multimodal AI for higher quality media. With roughly ten years of experience across Google Research and DeepMind, he excels at turning research ideas into production-ready systems for image and video quality improvements. His open-source contributions to google-research/google-research, including MUSIQ preprocessing, image patch handling, and integration work with VILA and tfhub, demonstrate a hands-on ability to engineer scalable ML tooling. He holds a Bachelor’s degree in Computer Science from Tsinghua University and a Master’s degree in Computer Science from Stanford, where he also served as a Teaching Assistant for CS224N. Based in Palo Alto, California, he combines academic rigor with practical execution, continually pushing the boundaries of multimodal quality assessment and generated AI.
11 years of coding experience
9 years of employment as a software developer
Bachelor’s Degree, Computer Science, 91.7/100, Bachelor’s Degree, Computer Science, 91.7/100 at Tsinghua UniversityMaster’s Degree, Computer Science, 3.989, Master’s Degree, Computer Science, 3.989 at Stanford University
English, Chinese, Chinese
Stackoverflow
101reputation
16kreached
1answer
0questions
Github Skills (8)
computer-vision10
tensorflow210
machine-learning10
tensorflow10
python10
image-processing10
feature-selection6
scikit-learn6
Programming languages (3)
Github contributions (5)
google-research/google-research
Aug 2021 - Jun 2022
Google Research
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 7 comments in 10 months
Contributions summary:Junjie contributed code related to the MUSIQ model, specifically focusing on preprocessing operations for image patches within the `google-research/google-research` repository, which is focused on AI and machine learning research. The changes involve defining functions for resizing images, extracting patches, and generating positional embeddings. Furthermore, the user made corrections to VILA and the integration of tfhub and musiq, indicating ongoing model development and refinement.
googlemachine-learningai
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