Alireza Shafaei is a machine learning consultant and exited CTO with a PhD in ML/CV and over a decade of experience shipping production-grade computer vision and retrieval systems at scale. He has built teams 0→1→N, led Skylab through productized ML features to acquisition, and now advises and runs Rezulie Ventures while consulting at Across AI. His strengths span embeddings, retrieval/RAG, re-ranking, model evaluation, and efficient edge/batch inference, with proven experience handling 100M-transaction pipelines and millions of daily images. A pragmatic researcher, he bridges academic rigor and production constraints—publishing work during his UBC PhD while teaching and mentoring engineers. He also contributes to open-source imaging tooling, improving WebP transparency handling in Pillow, which reflects his attention to robustness in real-world systems.
11 years of coding experience
11 years of employment as a software developer
Amirkabir University of Technology
Doctor of Philosophy (Ph.D.), Machine Learning - Computer Vision, Doctor of Philosophy (Ph.D.), Machine Learning - Computer Vision at The University of British Columbia
Contributions:3 reviews, 11 commits, 1 PR in 3 days
Contributions summary:Alireza primarily contributed to the enhancement of WebP image format support within the Pillow library. Their work involved adding a new "exact" parameter to preserve transparent RGB values during WebP encoding, modifying the C code for WebP integration, and updating the documentation. They also added tests to ensure the correct handling of transparency in WebP images and integrated the "exact" parameter with version checks.
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