Kyle Dorman

Kyle Dorman is a Machine Learning Engineer with extensive experience across various roles in machine learning and computer vision. Currently employed at Augmodo, Kyle has developed a comprehensive ML roadmap, built a cloud-based inference pipeline for retail product detection, and designed innovative auto-labeling and classification systems. Previous experience at Rapid Robotics, Inc. involved enhancing a keypoint estimation model's performance and implementing data collection processes for 3D localization. At Standard Cognition, accomplishments include architecting a human keypoint estimation framework and significantly improving model accuracy in retail environments. Earlier roles at Gilt.com involved contributions to frontend, iOS, and backend development, alongside administering an apprentice program, and an initial position at EnerNOC focused on application support. Kyle holds a degree in Biological and Environmental Engineering from Cornell University.