Aliu T.

Key Skills

Software

Mercor

OneForma

Toloka

Telus

Top Subject Matter

AI training data labeling

content moderation

and data verification

Top Data Types

Text

Image

Audio

Top Task Types

Computer Programming/Coding

Transcription

Evaluation/Rating

Question Answering

Classification

Freelancer Overview

Frontend Developer & Data Operations Specialist (Cakeneeds) — data labeling and AI training quality assurance work.. Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Google Sheets. Education includes Bachelor of Science, University of Ilorin (2024). AI-training focus includes data types such as Text and labeling workflows including Entity (NER), Evaluation, and Rating.

Labeling Experience

Frontend Developer & Data Operations Specialist (Cakeneeds) — data labeling and AI training quality assurance work.

Text

Performed large-scale text data labeling and QA for structured digital content, ensuring adherence to detailed annotation guidelines and maintaining high accuracy. Conducted guideline-based verification to identify errors, inconsistencies, and deviations while supporting inter-rater reliability and consistent moderation decisions. Worked with JSON and rule/judgment-based reasoning to evaluate nuanced classifications for classification and moderation tasks.• Text annotation and classification (sentiment, intent, topics, NER) following strict guidelines.• Content moderation/safety labeling for user- and AI-generated content against policy rules.• RLHF-style evaluation and rating of outputs (helpfulness/accuracy/instruction-following) for preference signals.• Quality assurance checks and dataset integrity verification using structured documentation.

2024 - Present

Junior Frontend Developer / Intern (The Print World) — verification and content QA supporting annotation workflows.

Text

Completed data verification and content quality checks on large digital record sets, focusing on accuracy-focused, instruction-driven annotation workflows. Sustained consistent output quality on repetitive, guideline-based tasks over extended periods and communicated findings clearly in structured documentation. Supported QA and verification steps that mirror professional labeling and inter-rater reliability expectations.• Data verification and content quality review for large datasets.• Guideline-based labeling execution with sustained accuracy over time.• Structured documentation and communication of QA findings.• Repetitive instruction-driven tasks aligned with annotation pipeline requirements.

2019 - 2020

Education

Work History