Raymond Wu is a tenacious software engineer and Make School fellow with eight years of hands-on experience building ML-driven products, from PyTorch computer vision models detecting potholes to style-transfer and CoreML mobile classifiers. He blends full-stack web development (React, Flask, Docker, AWS) with deep learning expertise (GANs, ResNet, U-Net, NLP RNNs) and a history of shipping production integrations such as Kepler.gl enhancements used by Unfolded Studio. Raymond has practical experience improving performance and workflows—e.g., nearly 100% speed gains in Kepler.gl by eliminating extraneous rendering—and regularly presents technical talks to engineering audiences. He’s contributed to decentralized governance UI work on Solana and built mapping integrations with OpenStreetMap and Mapillary, showing an aptitude for geospatial pipelines. A former leader in IP enforcement and operations, he brings investigative rigor and process automation skills to engineering problems. Raymond is seeking junior-to-mid software engineering roles where machine learning is integral to product or process.
8 years of coding experience
2 years of employment as a software developer
Cal Poly Pomona
Deep Learning I, Deep Learning I at University of San Francisco
Bachelor's degree, Applied Computer Science, Bachelor's degree, Applied Computer Science at Make School
Machine Learning, Machine Learning at Stanford University
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