Temitope Adebayo

Key Skills

Software

CVAT

Roboflow

Top Subject Matter

No subject matter listed

Top Data Types

Document

Text

Top Label Types

Evaluation Rating

Prompt Response Writing SFT

Question Answering

Text Generation

Text Summarization

Freelancer Overview

I am an experienced AI evaluator and QA specialist with a strong background in data annotation, instruction following, and logical reasoning analysis. My work involves Annotate and label various types of data (e.g., text, images and video) according to specified guidelines and requirements. I have hands-on experience using CVAT and Roboflow for image and video labeling, tasks such as object detection, segmentation, classification, bounding boxes, and frame-by-frame video annotation. For text annotation, I am proficient with Datasaur, handling tasks like instruction following, text classification, and logical reasoning analysis. Ensure high-quality annotations and data labeling that meet accuracy and consistency standards through rigorous Quality Control processes. Develop and implement Quality Control measures and procedures for annotated data. Collaborate with the data science and engineering teams to understand annotation requirements and contribute to the improvement of annotation processes. Work efficiently to meet deadlines while maintaining the quality and accuracy of annotated data. Report issues or inconsistencies in the data and suggest improvements in annotation guidelines or tools. Maintain proper documentation and records of annotated data and processes.

Labeling Experience

AI Response Evaluation | Text & Instruction Following

DatasaurTextQuestion AnsweringText Generation

Evaluated AI-generated responses across multiple prompts for accuracy, instruction compliance, logical reasoning, and content safety. Compared multiple AI outputs, ranked responses based on quality, clarity, and adherence to guidelines. Identified hallucinations, incomplete answers, and inconsistencies, documenting findings in structured evaluation notes. Practiced QA-style assessment techniques to ensure consistency and reliability across tasks. Simulated end-to-end AI evaluation workflows to prepare for real-world data labeling projects.

Evaluated AI-generated responses across multiple prompts for accuracy, instruction compliance, logical reasoning, and content safety. Compared multiple AI outputs, ranked responses based on quality, clarity, and adherence to guidelines. Identified hallucinations, incomplete answers, and inconsistencies, documenting findings in structured evaluation notes. Practiced QA-style assessment techniques to ensure consistency and reliability across tasks. Simulated end-to-end AI evaluation workflows to prepare for real-world data labeling projects.

2025

Multimodal AI Annotation Specialist

RoboflowVideoBounding BoxPolygon

Project Scope: Contract-based video annotation project focused on household chores to support human activity recognition and smart home AI systems. Data Tasks Performed: Annotated short videos using Atlas Capture, labeling actions such as cleaning, cooking, laundry, and organizing. Performed frame-level activity tagging, timestamp segmentation, and validation of participant-submitted videos from Prolific. Project Size: Annotated 150+ household activity videos, covering multiple chore categories and real home environments. Quality Measures: Strict adherence to project guidelines with multi-pass review, consistency checks, and spot audits, achieving high annotation accuracy and on-time delivery.

Project Scope: Contract-based video annotation project focused on household chores to support human activity recognition and smart home AI systems. Data Tasks Performed: Annotated short videos using Atlas Capture, labeling actions such as cleaning, cooking, laundry, and organizing. Performed frame-level activity tagging, timestamp segmentation, and validation of participant-submitted videos from Prolific. Project Size: Annotated 150+ household activity videos, covering multiple chore categories and real home environments. Quality Measures: Strict adherence to project guidelines with multi-pass review, consistency checks, and spot audits, achieving high annotation accuracy and on-time delivery.

2025 - 2025

AI Training Data Specialist – Image, Video & Text Labeling

RoboflowImageBounding BoxPolygon

Annotated image and video datasets of chickens for a computer vision poultry monitoring system. The objective was to enable accurate detection, classification, and behavior analysis in farm environments. Data Tasks Performed: Image and video annotation using CVAT and Roboflow, including bounding boxes, object classification, and frame-by-frame video labeling. Performed quality checks, corrected mislabels, and validated annotations against project guidelines. Project Size: Annotated 3,000+ images and 120+ short video clips across multiple lighting and background conditions. Quality Measures: Followed strict annotation guidelines with peer review, spot checks, and consistency validation, maintaining >91% annotation accuracy and meeting all delivery timelines.

Annotated image and video datasets of chickens for a computer vision poultry monitoring system. The objective was to enable accurate detection, classification, and behavior analysis in farm environments. Data Tasks Performed: Image and video annotation using CVAT and Roboflow, including bounding boxes, object classification, and frame-by-frame video labeling. Performed quality checks, corrected mislabels, and validated annotations against project guidelines. Project Size: Annotated 3,000+ images and 120+ short video clips across multiple lighting and background conditions. Quality Measures: Followed strict annotation guidelines with peer review, spot checks, and consistency validation, maintaining >91% annotation accuracy and meeting all delivery timelines.

2025 - 2025

Education

Work History