Oluwabusayo Adetunlese

Key Skills

Software

AWS SageMaker

Appen

Clickworker

CVAT

Labelbox

Scale AI

SuperAnnotate

Other

Label Studio

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Label Types

Bounding Box

Classification

Segmentation

Text Generation

Text Summarization

Freelancer Overview

I am an AI Data Trainer with strong experience in image annotation, UX/UI labeling, and text evaluation, supported by 4+ years of design, UI/UX, and analytical work. My background in visual systems, civil engineering, and content analysis has equipped me with high accuracy, pattern recognition skills, and a structured approach to data labeling. I have worked extensively with visual content, user flows, and text-based evaluation, allowing me to contribute effectively to computer vision, multimodal tasks, and LLM evaluation projects. I have hands-on experience with multiple annotation environments, including Label Studio, CVAT, SuperAnnotate, Labelbox, and Figma for UI component labeling. My work often involves categorizing visuals, identifying UI elements, evaluating text clarity, scoring model outputs, and providing human feedback to improve AI behavior. I am reliable, detail-oriented, and committed to producing high-quality labeled datasets. I bring a strong blend of visual accuracy, reasoning, and user-centered judgment to every project, and I am open to freelance, contract-based, and full-time AI training roles.

Labeling Experience

LLM Text Evaluation and Output Ranking

LabelboxTextText SummarizationEvaluation Rating

Reviewed and evaluated AI-generated responses for correctness, clarity, tone, and instruction alignment. Performed ranking tasks by comparing multiple model outputs. Labeled text for safety, relevance, and user intent classification. Helped improve language model performance by providing consistent, high-quality human feedback.

Reviewed and evaluated AI-generated responses for correctness, clarity, tone, and instruction alignment. Performed ranking tasks by comparing multiple model outputs. Labeled text for safety, relevance, and user intent classification. Helped improve language model performance by providing consistent, high-quality human feedback.

2024

UX/UI Interface Component Annotation

Label StudioImageBounding BoxClassification

Annotated UI components such as buttons, icons, text fields, navigation bars, and interactive elements for a digital product dataset. Labeled visual elements with bounding boxes and classification tags to support model understanding of interface structures and user intent. Ensured accuracy, consistency, and alignment with detailed annotation guidelines.

Annotated UI components such as buttons, icons, text fields, navigation bars, and interactive elements for a digital product dataset. Labeled visual elements with bounding boxes and classification tags to support model understanding of interface structures and user intent. Ensured accuracy, consistency, and alignment with detailed annotation guidelines.

2023 - 2023

Image Annotation for Visual Content Classification

CVATImageBounding BoxPolygon

Labeled images containing branding elements, objects, backgrounds, and design components. Drew bounding boxes and polygons to mark objects and applied classification labels based on design style, type, and context. Supported dataset preparation for computer vision models requiring high visual precision.

Labeled images containing branding elements, objects, backgrounds, and design components. Drew bounding boxes and polygons to mark objects and applied classification labels based on design style, type, and context. Supported dataset preparation for computer vision models requiring high visual precision.

2022 - 2023

Education

Work History