Terry Rodriguez
Co-Founder, CTO at Remyx AI
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolTerry Rodriguez is a founder and CTO with a decade of hands-on experience building AI/ML systems and the experiment intelligence layer at Remyx AI. Based in San Francisco, he blends applied research and product engineering from roles at Robust.AI, Tubi, and Formant to translate ML experiments into reliable production workflows. His background in mathematics (UC Berkeley, UNC Chapel Hill) underpins a rigorous approach to modeling and evaluation, while his consulting work at Smells Like ML keeps him close to practical tooling and developer needs. On GitHub he has implemented real-time, spatio-temporally localized activity detection—bringing pose extraction, lightweight inference with TensorFlow Lite, and webcam-based pipelines together for action recognition. Known for shipping pragmatic ML solutions, he bridges research-grade models and production constraints to accelerate experimentation velocity. Colleagues describe him as an entrepreneurial engineer who pairs technical depth with a focus on measurable experiment outcomes.
10 years of coding experience
7 years of employment as a software developer
Master's degree, Mathematics, Master's degree, Mathematics at University of California, BerkeleyBachelor of Science (BS), Mathematics, Bachelor of Science (BS), Mathematics at The University of North Carolina at Chapel Hill
Korean, Spanish
Github Skills (11)
computer-vision10
opencv10
machine-learning10
tensorflow10
python10
pose-estimation9
scikit-learn9
scikit9
lstm9
tensorflow-lite9
raspberry-pi5
Programming languages (5)
Github contributions (5)
smellslikeml/ActionAI
Jun 2019 - Oct 2022
Real-Time Spatio-Temporally Localized Activity Detection by Tracking Body Keypoints
Role in this project:
ML Engineer
ML EngineerContributions:112 commits, 20 PRs, 77 pushes in 3 years 4 months
Contributions summary:Terry's primary contributions focus on developing a real-time spatio-temporally localized activity detection system. They implemented core components for pose extraction using a TensorFlow Lite model and integrated it into a pipeline for action recognition. The user also worked on training a classifier using Logistic Regression and integrating the model for inference on webcam video feeds using OpenCV. Contributions include the initial setup, configuration, and core logic implementation.
mlpheart-rateclassifiersiamese-networkpong
smellslikeml/cnn_eeg
Feb 2019 - Jan 2020
Contributions:10 commits, 5 pushes, 1 branch in 11 months
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