Ruiqing Qiu
Member Of Technical Staff at Microsoft AI
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolRuiqing Qiu is a Member of Technical Staff at Microsoft AI with 11 years of experience building production-grade AI and media products, previously leading GenAI, sports infrastructure, and live TV experiences at YouTube. He combines full-stack engineering and ML/NLP expertise—from Scala/Python data pipelines and recommendation engines to LLM-driven metadata and multi-modal video understanding—to ship scalable systems used by millions. Notable projects include launching NFL Sunday Ticket features like multiview and real-time telestration powered by NFL Next Gen Stats, and contributing to the Apache PredictionIO ecosystem. A creative technologist outside work, he hosts an AI-generated podcast, designs commercial prompts on PromptBase, and experiments with generative visuals and consumer apps, reflecting a pragmatic maker’s instinct for turning research into delightful, usable products. Located in Mountain View, he blends product-focused leadership with hands-on implementation across backend, ML, and client experiences.
11 years of coding experience
8 years of employment as a software developer
University of California, San Diego
English, Chinese, Japanese
Github Skills (15)
predictionio10
python10
scala10
machine-learning9
big-data9
algorithms8
data-structures8
algorithm8
data-structure8
data-pipeline8
data-pipelines8
restful-api7
api-design7
api-rest7
rest-api7
Programming languages (1)
Github contributions (5)
apache/predictionio
Oct 2014 - Dec 2014
PredictionIO, a machine learning server for developers and ML engineers.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:38 commits in 1 month
Contributions summary:Ruiqing primarily contributed to the development of a friend recommendation engine. They implemented data source integrations for reading and processing training data using Scala and Python. Additionally, the user added command-line arguments to a file processing script, enabling greater flexibility. Moreover, they updated and added data sources, and added code related to recommendation algorithms in Scala. The user also added an evaluator for the recommendations, showing their involvement in the entire recommendation pipeline.
pythondata-sciencemachine-learningmlopsbig-data
RuiqingQiu/K-Mean
Jan 2015 - May 2015
Contributions:58 commits, 39 pushes, 1 branch in 4 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.