Musa D.

Key Skills

Software

Mercor

Data Annotation Tech

OneForma

Top Subject Matter

AI training data (video/image) annotation QA

Frontend support for annotation workflows

Legal Services & Contract Review

Top Data Types

Computer Code Programming

Text

Video

Image

Document

Top Task Types

Computer Programming/Coding

Text Summarization

Question Answering

Text Generation

Object Detection

Data Collection

Freelancer Overview

Data Annotation Expert (video & image annotation, timestamp validation, annotation QA) at Atlas Capture. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include N, A, and GitHub. Education includes Bachelor of Technology, Ladoke Akintola University of Technology (2021). AI-training focus includes data types such as Video, Computer Code, and Programming and labeling workflows including Evaluation, Rating, and Computer Programming.

Labeling Experience

Freelance Frontend Developer (annotation workflow UI support)

Contributed to AI training workflows indirectly by supporting structured data-related annotation processes through frontend tools and interfaces. Developed and tested UI components to improve usability and responsiveness for annotation-related interactions. Applied JavaScript and frontend best practices to assist remote collaboration and workflow efficiency. • Developed UI components using JavaScript • Implemented responsive layouts with Tailwind CSS • Used GitHub and Visual Studio Code for workflow/version control • Supported remote collaboration via project management

2025 - Present

Video Data Annotation

VideoSegmentation

Worked on video annotation tasks using Atlas Capture, focusing on accurately labeling objects and actions across video frames for machine learning datasets. The project involved identifying and tracking relevant entities, applying bounding boxes and tags consistently across sequences, and ensuring frame-level accuracy. Handled datasets of varying complexity, ranging from simple single-object videos to multi-object and motion-based scenarios. Maintained high annotation quality by following strict labeling guidelines, ensuring consistency, and reviewing outputs to reduce errors. Adhered to quality assurance standards including precision checks, frame validation, and guideline compliance to ensure reliable training data for model development.

2025 - Present

Education

Work History