Summary
Michael Sugimura is a machine learning engineer with eight years of experience applying computer vision, NLP, and deep learning to e-commerce, public sector, and research problems. He has led production ML efforts—building multi-task, multi-modal ensembles, object detection/segmentation pipelines, and GAN prototypes—at companies like ShopRunner, Wayfair, and LTK, and now runs two ventures including a research-focused role at 2389 Research. His background in public policy (Master’s from Georgetown) and linguistics (BA from Dartmouth) gives him an uncommon blend of technical rigor and domain-driven problem framing, especially for messy unstructured data. He has tackled applied challenges from counterfeit art detection with Siamese networks to anti-human-trafficking analytics, and he open-sourced tooling for large-scale multi-dataset training. Colleagues describe him as an engineer who moves models from experiment to production while keeping an eye on practical impact and interpretability.
9 years of coding experience