Lead applied computer vision research and develop end-to-end perception systems for autonomous driving, taking research from loosely defined directions through validated POCs toward production adoption.
Own and scale production computer-vision systems for sports video, from detection, tracking and action recognition to model evaluation and GPU inference optimization across high-volume streams.
Develop and improve computer vision models for real-world traffic detection and tracking, owning the full cycle from data strategy and reproducible training to field validation and edge deployment.
Lead the design and deployment of computer vision and machine learning systems for industrial operations, shaping model strategy, platform architecture, and engineering practices across a high-ownership team.
Own the end-to-end development and production deployment of visual inspection solutions, building and optimizing computer vision and deep learning models for reliable, high-throughput defect detection on factory automation lines.
