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Clickworker
CloudFactory
CrowdSource
Dataloop
Labelbox
Roboflow
Sama
Telus
Remotasks
Scale AI
CVAT
Computer Code Programming
Image
VideoWith a focus on tasks including picture and video annotation, polygon labeling, chatbot AI training, and AI verification, I have over 5 years of experience in data labeling and AI training. I began working at Remotask in 2019 as a Tasker and swiftly progressed through the ranks to become a Team Lead, overseeing a group of people and making sure that machine learning models in a variety of industries had high-quality, effective data labeling. With a performance level of 98%+ accuracy, my work has contributed to initiatives in computer vision, natural language processing, and AI verification. I have experience with a variety of datasets, including photos, videos, and intricate AI models, and am skilled in both human and automated annotation procedures. I've honed my attention to detail and built a strong focus on quality control, workflow optimization, and team leadership. With my leadership expertise and AI training data background, I am well-positioned to contribute significantly to any AI or machine learning project, guaranteeing accuracy and scalability.
Attention to detail, ensuring 98%+ accuracy in data and maintaining consistency across large, complex datasets.
This experience has given me a solid foundation in object detection, image annotation, and video labeling, especially in the context of urban infrastructure, road safety, and autonomous vehicle datasets. I have worked on projects involving both simple and complex labeling tasks and have adhered to strict quality control processes to ensure the accuracy of all annotations.
This project involved labeling a large dataset of video footage captured by autonomous vehicles, with a focus on accurately annotating objects, pedestrians, vehicles, and road infrastructure. Tasks included using bounding boxes and polygons to outline key objects, object detection for identifying and classifying different vehicle types, and tracking for continuous movement across video frames. In addition, segmentation was applied to distinguish between road surfaces and obstacles. I led a team of 5 in ensuring accurate data labeling, maintained a high-quality standard with 98%+ accuracy, and followed strict guidelines for quality control and consistency across the annotations. The project involved over 10,000 video frames and required collaboration across multiple teams to meet tight deadlines while maintaining high precision.
This project involved labeling a large dataset of video footage captured by autonomous vehicles, with a focus on accurately annotating objects, pedestrians, vehicles, and road infrastructure. Tasks included using bounding boxes and polygons to outline key objects, object detection for identifying and classifying different vehicle types, and tracking for continuous movement across video frames. In addition, segmentation was applied to distinguish between road surfaces and obstacles. I led a team of 5 in ensuring accurate data labeling, maintained a high-quality standard with 98%+ accuracy, and followed strict guidelines for quality control and consistency across the annotations. The project involved over 10,000 video frames and required collaboration across multiple teams to meet tight deadlines while maintaining high precision.
Diploma, ICT
Certificate, Financial Analyst
Quality Assurance Analyst
Quality Analyst,Research Associate and Data Management Specialist
