Strong science and/or engineering background (e.g., computer vision, machine learning, robotics, signal processing, or related field), with the ability to evaluate and guide both research and engineering decisions - We use cutting-edge machine learning models deployed on custom hardware to enable high-quality image acquisition, identification, and fraud prevention, all while requiring minimal user interaction. * Own the vision, roadmap, and execution for World's biometric recognition stack (iris/face recognition and related fraud defenses), balancing research innovation with production reliability * Partner on the technical vision and strategy with hardware, product, and security teams to ensure tight integration between sensing, image acquisition, on-device inference, and backend systems * Demonstrated experience with modern and traditional computer vision / ML, and a deep ...
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