For employers

Hire this AI Trainer

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

Invite to Job
M
Mark W.

Mark W.

AI Training Data Annotator | Text & Content Evaluation

Kenya flagNairobi, Kenya

Key Skills

Software

No software listed

Top Subject Matter

Research / Information Evaluation

Top Data Types

AudioAudio
TextText
ImageImage

Top Task Types

TranscriptionTranscription
Audio RecordingAudio Recording
Data CollectionData Collection
Text SummarizationText Summarization
Emotion RecognitionEmotion Recognition

Freelancer Overview

Here is a professional sample you can adapt: I have experience working with AI-related tasks that involve data annotation, content evaluation, and quality assessment. My work has included reviewing text for accuracy, relevance, grammar, and consistency, as well as comparing AI-generated responses and providing feedback to improve model performance. I am comfortable following detailed guidelines, identifying errors, and ensuring that labeled data meets quality standards. I have also completed tasks involving transcription, categorization, and sentiment analysis while maintaining attention to detail and accuracy. In addition, I have strong research, communication, and analytical skills developed through my academic background and online freelance projects. I am proficient in English and can evaluate content from different perspectives to ensure it aligns with project requirements. My ability to learn new instructions quickly, work independently, and consistently deliver high-quality results makes me well-suited for AI training and data labeling projects.

Labeling Experience

AI Data Annotation & Training Support Assistant

TextTextEntity (NER) ClassificationEntity (NER) Classification

Continuing to support AI training tasks involving text annotation, dataset labeling, and evaluation of model outputs. The work focuses on improving machine learning datasets through accurate entity recognition, classification, and prompt-response structuring. Tasks include reviewing AI-generated responses for correctness, relevance, and safety, as well as preparing high-quality examples for supervised fine-tuning (SFT). The project emphasizes consistency, attention to detail, and adherence to annotation guidelines to help improve overall model performance and reliability.

2025 - Present

Education

B

Bsc Information and Technology Nairobi University

Degree not specified

Not specified
Not specified

Work History

C

Company not specified

AI Data Annotator

Location not specified
Not specified