Philomena Maweu

Philomena Maweu is a highly skilled Data Annotation Specialist with over three years of experience contributing to the development of advanced artificial intelligence systems. Based in Kenya and working remotely with global teams, she specializes in creating high-quality labeled datasets for computer vision and autonomous driving applications. Her expertise spans both 2D and 3D annotation, including LiDAR segmentation, object detection, bounding boxes, cuboids, and sequence tracking across complex multi-sensor environments.

Throughout her career, Philomena has worked with leading annotation platforms such as CVAT, Labelbox, Supervisely, and LabelCloud, consistently delivering precise and reliable outputs. She has played a key role in large-scale projects involving autonomous vehicles, where accuracy and consistency are critical to safety and system performance. Her work routinely achieves QA acceptance rates above 97%, reflecting her strong attention to detail and commitment to excellence.

In addition to her technical capabilities, Philomena has demonstrated leadership and collaboration skills. She has trained junior annotators, conducted quality assurance reviews, and contributed to refining annotation guidelines to improve team efficiency and dataset consistency. Her experience also includes working on text annotation projects involving sentiment analysis, intent classification, and disinformation detection, showcasing her versatility across different data types.

Philomena holds a Bachelor of Education in Arts, with a focus on linguistics and communication, which enhances her ability to interpret complex annotation guidelines and produce structured, accurate data. She is fluent in English and a native speaker of Kiswahili and Kamba. Passionate about human-in-the-loop AI systems, Philomena is dedicated to improving model performance through high-quality data labeling while promoting fairness and reliability in AI technologies.