Fayej Sheikh

Data Annotation and Quality Associate with 2.7+ years of experience supporting AI/ML and Autonomous Vehicle projects through image and video annotation, quality assurance, and auditing. Experienced in reviewing complex visual data, applying annotation guidelines, validating model outputs, identifying defects, and consistently exceeding quality and productivity targets. Skilled in semantic segmentation, bounding boxes, object tracking, and ensuring high-quality training data for autonomous driving systems.

Operations Associate – Data Quality & Annotation at Uber (2023-10 – 2026-03)

• Maintained 99.8% defect detection accuracy while reviewing AI assets and annotated data.
• Annotated and validated autonomous vehicle (AV) image and video datasets for AI/ML model training.
• Performed object detection, lane marking, traffic sign, pedestrian, vehicle, and road feature annotation.
• Executed bounding box and semantic segmentation tasks with 98.5% precision.
• Reviewed edge cases involving occlusions, poor lighting, and complex traffic scenarios.
• Maintained 98–99% annotation accuracy while meeting productivity targets.
• Improved workflow efficiency by 15% through process improvements.
• Collaborated with cross-functional teams to refine annotation guidelines.

MBA – GH Raisoni Institute of Management & Research (2021 – 2023)

BCCA – Tirpude College of Management (2018 – 2020)