Rachel Blin is an AI research engineer with eight years of experience bridging academic deep learning research and production-grade multimodal AI systems. After a PhD focused on polarimetric imaging and robust object detection in adverse road conditions, she applied those image-processing skills at Jellysmack to build video understanding pipelines spanning vision, audio, and language for content safety, highlight detection, and recommendation tasks. She combines hands-on expertise in PyTorch, AWS, and dataset engineering with a track record of designing constrained CycleGANs and publishing scientific work, bringing rigor from research into fast-moving product environments. Now at Helsing, she continues to translate novel sensing and multimodal methods into deployable solutions, pairing a researcher’s curiosity with practical experience in annotation, scalability, and model deployment. An uncommon asset is her background in polarimetric imagery, which gives her a distinctive edge in sensing-aware detection problems.
8 years of coding experience
3 years of employment as a software developer
First year of medical studies, First year of medical studies at Université Pierre et Marie Curie
PhD, Computer Science, PhD, Computer Science at Institut National des Sciences Appliquées de Rouen
Baccalauretate, Scientific, mathematics option, with Honours, Baccalauretate, Scientific, mathematics option, with Honours at Lycée Français Dominique Savio, Douala Cameroun
Contributions:21 commits, 19 pushes, 1 branch in 1 year 2 months
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