Jayeola Taofeek

Key Skills

Software

Appen

Clickworker

OneForma

Remotasks

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Medical Dicom

Video

Top Task Types

Evaluation Rating

Prompt Response Writing SFT

Text Summarization

Translation Localization

Freelancer Overview

I am an expert AI training data contributor with a strong background in text and image annotation, LLM prompt evaluation, Video data labelling and English language review. I’ve worked on diverse AI projects involving text classification, sentiment labeling, object recognition, and prompt quality scoring, ensuring data accuracy and alignment with complex guidelines. Proficient in tools such as Labelbox, SuperAnnotate, and Google Sheets, I consistently deliver high-quality annotations for both natural language processing and computer vision tasks. I have evaluated and refined prompts for large language models, detecting ambiguity, bias, and relevance issues to improve model performance. My strengths include attention to detail, language proficiency, adherence to style guides, and the ability to meet deadlines in fast-paced, remote environments. With hands-on experience across platforms like Appen, Scale AI, I bring efficiency, accuracy, and consistency to every task.

Labeling Experience

RENRT

AppenVideoQuestion AnsweringObject Detection

• Annotated soccer video clips, identifying player actions and labeling in-game events • Tagged images and short videos for object detection, emotion classification, and scene context • Composed concise, descriptive captions for video actions in line with project-specific taxonomies • Reviewed and rated LLM prompts, assessing linguistic quality, relevance, and instruction-following • Delivered projects with >95% accuracy and strict adherence to annotation style guides

• Annotated soccer video clips, identifying player actions and labeling in-game events • Tagged images and short videos for object detection, emotion classification, and scene context • Composed concise, descriptive captions for video actions in line with project-specific taxonomies • Reviewed and rated LLM prompts, assessing linguistic quality, relevance, and instruction-following • Delivered projects with >95% accuracy and strict adherence to annotation style guides

2023 - 2024

BERNT

Scale AIImageBounding BoxPolygon

• Fashion Video Labeling: Clothes annotation, gameplay action recognition •Image & Visual Data: Object tagging, emotion detection, scene classification •Text & Language Review: LLM prompt rating, grammar/style evaluation •AI Training Data Quality: NLP & vision datasets for leading AI platforms

• Fashion Video Labeling: Clothes annotation, gameplay action recognition •Image & Visual Data: Object tagging, emotion detection, scene classification •Text & Language Review: LLM prompt rating, grammar/style evaluation •AI Training Data Quality: NLP & vision datasets for leading AI platforms

2023 - 2023

Medical Imaging Annotator

CVATMedical DicomBounding BoxPolygon

Contributed to a healthcare AI project on the Appen platform, labeling DICOM-format medical images (CT, MRI, X-rays) to train diagnostic models. Tasks included bounding box, segmentation, and classification of anatomical structures and abnormalities such as tumors and fractures. Worked with over 10,000 medical images, collaborating with a remote team of 50+ annotators. Ensured high-quality outputs through QA reviews, inter-annotator alignment, and adherence to HIPAA-aligned standards. Maintained >95% annotation accuracy while following strict medical imaging protocols and project-specific guidelines.

Contributed to a healthcare AI project on the Appen platform, labeling DICOM-format medical images (CT, MRI, X-rays) to train diagnostic models. Tasks included bounding box, segmentation, and classification of anatomical structures and abnormalities such as tumors and fractures. Worked with over 10,000 medical images, collaborating with a remote team of 50+ annotators. Ensured high-quality outputs through QA reviews, inter-annotator alignment, and adherence to HIPAA-aligned standards. Maintained >95% annotation accuracy while following strict medical imaging protocols and project-specific guidelines.

2021 - 2023

Education

Work History