Timilehin D.

Key Skills

Software

Appen

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Text

Audio

Top Task Types

RLHF

Evaluation/Rating

Freelancer Overview

With over 2 years of hands-on experience in AI training data and data labeling, I have critically evaluated and refined thousands of AI-generated outputs across text, audio, and multimodal content. At Mindrift (2024–Present), I assess AI responses for quality, accuracy, clarity, reasoning coherence, and policy compliance, delivering nuanced feedback that directly contributes to model improvement. Previously at Scale AI (2022–2023), I reviewed over 5,000 training responses, developed evaluation rubrics, and maintained 95%+ quality scores. At Appen, I evaluate 12,000+ audio and content samples per quarter with a consistent 98.7% accuracy rate, identifying data integrity issues and helping improve downstream model reliability. What sets me apart is my combination of strong linguistic training (Bachelor’s in English Language and Linguistics), editorial judgment, and deep familiarity with RLHF, SFT, and structured evaluation workflows. I excel at detecting hallucinations, inconsistencies, and subtle quality issues while providing actionable, well-documented feedback. My cross-platform experience (Mindrift, Scale AI, Appen, Remotasks) and certifications in Natural Language Processing and Google Data Analytics enable me to quickly adapt to new guidelines and contribute effectively to high-volume, high-stakes AI training projects.

Labeling Experience

AI Output Evaluation & Training Data Specialist at Mindrift

TextRLHF

Served as an AI Trainer and Writer at Mindrift, critically evaluating and scoring thousands of AI-generated outputs for quality, clarity, accuracy, logical coherence, factual correctness, and user experience. Applied structured evaluation rubrics to identify hallucinations, reasoning failures, policy violations, and inconsistencies. Delivered nuanced, actionable feedback that directly contributed to model improvement. Collaborated with QA teams to refine scoring criteria and evaluation guidelines. Maintained consistently high accuracy (95–98%) across high-volume, fast-paced remote projects. Also contributed to training data development by producing well-structured, fact-checked responses.

2025 - Present