Odumade Adeshina

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Object Detection

Action Recognition

Data Collection

Freelancer Overview

I am an experienced AI content specialist and data annotator with a strong background in evaluating and enhancing AI-generated content, particularly for large language models (LLMs). My work focuses on data labeling, RLHF, and fact-checking to ensure high standards of quality, consistency, and safety. I am skilled in technical editing, intent-checking, and providing detailed, actionable feedback to improve AI fluency and realism. My expertise spans various tools including Canva, WordPress, Google Workspace, Excel, and multiple communication platforms. With a BSc in Computer Science and certifications in AI and digital content, I am dedicated to optimizing AI training data and supporting the development of accurate, culturally relevant, and engaging AI systems.

Labeling Experience

Data Annotation

AppenImageObject DetectionAction Recognition

Data Annotation: Label, tag, classify, and annotate datasets (images, text, video, audio) based on project guidelines. Quality Control: Review and validate labeled data for accuracy to ensure high-quality, clean training data. Rule Application: Adhere to complex, detailed guidelines and taxonomies, often for computer vision, NLP, or AI models. Tool Proficiency: Utilize data annotation software (e.g., Labelbox, SuperAnnotate, CVAT) efficiently. Collaboration: Provide feedback to ML engineers on ambiguities or potential improvements in labeling instructions. Efficiency: Meet strict productivity and quality targets within project deadlines.

Data Annotation: Label, tag, classify, and annotate datasets (images, text, video, audio) based on project guidelines. Quality Control: Review and validate labeled data for accuracy to ensure high-quality, clean training data. Rule Application: Adhere to complex, detailed guidelines and taxonomies, often for computer vision, NLP, or AI models. Tool Proficiency: Utilize data annotation software (e.g., Labelbox, SuperAnnotate, CVAT) efficiently. Collaboration: Provide feedback to ML engineers on ambiguities or potential improvements in labeling instructions. Efficiency: Meet strict productivity and quality targets within project deadlines.

2022 - 2025

Education

Work History