Chinedu O.

Key Skills

Software

Appen

Top Subject Matter

Machine learning and video labeling for AI model training

Quality assurance for AI/product outputs

AI training data labeling and human feedback

Top Data Types

Video

Image

Text

Document

Top Task Types

Fine-tuning

Bounding Box

RLHF

Object Detection

Segmentation

Freelancer Overview

AI Data Annotator at Atlas (Remote). Brings 10+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other and Appen. Education includes Bachelor of Engineering, Federal University of Agriculture, Abeokuta (2021). AI-training focus includes data types such as Video, N, and A and labeling workflows including Evaluation, Rating, and Fine-tuning.

Labeling Experience

AI Data Annotator at Atlas (Remote)

OtherVideo

Trained machine learning models by accurately labeling video data to improve model performance. Performed quality-focused review of annotations to ensure consistency with task guidelines and reduce labeling errors. Supported ongoing model improvement by providing reliable labeled outputs for downstream evaluation and iteration. • Labeled video segments accurately according to provided instructions • Verified annotation quality and corrected mistakes • Contributed to model training effectiveness through consistent labeling • Collaborated with remote task workflows and feedback cycles

2024 - Present

QA Analyst at Blend (Remote)

Other

Conducted product testing activities centered on assessing and ensuring quality for AI-related outputs. Analyzed results to identify issues, inconsistencies, and areas needing improvement in the delivered experience. Supported quality assurance efforts to help teams maintain reliable performance of deployed systems. • Performed product testing and quality checks • Analyzed outputs for accuracy and consistency • Reported findings to support improvement cycles • Ensured task deliverables met quality expectations

2023 - Present

AI Training Specialist at Appen (Remote)

AppenFine-tuning

Labeled AI training data and provided human feedback to correct errors, improving the accuracy of AI outputs. Performed iterative review of label quality to ensure the training dataset aligned with expected behaviors. Contributed directly to refining model responses through hands-on correction of annotation and feedback. • Created and updated labels for training data • Supplied human feedback to improve model output quality • Corrected errors found in AI-generated results • Supported training dataset accuracy through continuous review

2022 - 2023

Education

Work History