For employers

Hire this AI Trainer

Sign in or create an account to invite AI Trainers to your job.

Invite to Job
W
Winner E.

Winner E.

AI Data Annotator

Nigeria flagIbadan, Nigeria

Key Skills

Software

TolokaToloka
AppenAppen
RemotasksRemotasks
MindriftMindrift
MercorMercor
Micro1
Label StudioLabel Studio
LabelImgLabelImg
iMeritiMerit
HumanaticHumanatic
HiveMindHiveMind

Top Subject Matter

Research Industries and Customer Support

Top Data Types

VideoVideo
ImageImage
AudioAudio

Top Task Types

Bounding BoxBounding Box
PolygonPolygon
SegmentationSegmentation
ClassificationClassification
Object DetectionObject Detection
Question AnsweringQuestion Answering
Entity (NER) ClassificationEntity (NER) Classification
Point/Key PointPoint/Key Point
PolylinePolyline
TranscriptionTranscription
Data CollectionData Collection
Prompt + Response Writing (SFT)Prompt + Response Writing (SFT)
Red TeamingRed Teaming

Freelancer Overview

My journey in Training AI has been quite exploratory and eye-opening. With more than two years of hands-on experience in AI training data, I built a solid foundation in data annotation at AnnotateNow, where I created and labeled video datasets used to train and evaluate machine learning models. My work involved applying detailed annotation guidelines consistently across large volumes of content, performing quality checks, and flagging errors. All these are critical tasks that directly influence how well AI systems learn and perform. What sets me apart is the combination of practical annotation experience and an academic background in Adult Education from the University of Ibadan, Nigeria. Education by nature is about assessing understanding, identifying gaps, and giving structured feedback — skills that translate directly into AI evaluation and data quality work. This means I don't just label data mechanically, I bring an analytical, assessment-oriented mindset that is especially valuable in roles focused on AI evaluation, RLHF (Reinforcement Learning from Human Feedback), and training data quality.

Labeling Experience

Data Annotator

VideoVideoBounding BoxBounding Box

From my experience at AnnotateNow and Micro1, the scope of an AI project defines exactly what needs to be done and what the boundaries are. For example, when I worked on video labeling projects, the scope would specify what types of content we were annotating, how many videos were included, what labeling format to follow, and the deadline. It also made clear what was out of scope — for instance, we might only label certain object types and not others. Understanding the scope helped me stay accurate and consistent, because I knew exactly what was expected without going beyond or falling short of the project requirements.

2024 - 2026

Education

U

University of Ibadan, Nigeria

B.Ed, Adult Education

B.Ed
2016 - 2021

Work History

H

His LIneage Publishing

Manager

Ibadan
2022 - 2023