Summary
Iker Lopez is a computer vision engineer and PhD-trained researcher with a decade of experience building applied imaging and machine learning systems for industry and academia. He has developed end-to-end solutions from C++ mesh and 2.5D point-cloud reconstruction to deep learning segmentation and deployment on AWS and GCP, including U-Net models for OCT volumetric face-part segmentation at Meta. His background spans remote sensing multispectral classification, Kinect-based sign language recognition, VR 16k image stitching, and production-focused tooling for dataset curation and automated preprocessing. Comfortable across Python, C++, CUDA, and cloud ML services, he combines rigorous research methods with pragmatic engineering to accelerate dataset creation and model deployment. Based in the Basque Country, he seeks R&D roles that bridge novel computer vision algorithms and real-world productization, and notably brings hands-on experience improving annotation pipelines through traditional image enhancement techniques.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD, Computing, GPA 3.8, Doctor of Philosophy - PhD, Computing, GPA 3.8 at Boise State University
Bachelor's degree, Computer science, 7.4 / 10, Bachelor's degree, Computer science, 7.4 / 10 at UPV-EHU
Master's degree, Computer Science - Computing and Intelligent systems, 8.5 / 10, Master's degree, Computer Science - Computing and Intelligent systems, 8.5 / 10 at Universidad del País Vasco/Euskal Herriko Unibertsitatea
Basque, English, Spanish