Favour I.

Key Skills

Software

Don't disclose

Other

Mercor

Mindrift

OneForma

Telus

Top Subject Matter

AI training

LLM output evaluation

and alignment/rubric-based feedback

Top Data Types

Text

Document

Image

Top Task Types

Data Collection

RLHF

Evaluation/Rating

Prompt + Response Writing (SFT)

Object Detection

Bounding Box

Question Answering

Text Generation

Transcription

Freelancer Overview

Meticulous AI Trainer candidate (AI output evaluation, safety/factuality checks, rubric-followed feedback). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and Other. Education includes Bachelor of Science, University of Lagos (2024). AI-training focus includes data types such as Text and Document and labeling workflows including Evaluation, Rating, and Data Collection.

Labeling Experience

Data Analyst (data validation/cleaning tasks that support annotation/model input quality)

OtherDocumentData Collection

Performed data cleaning, validation, and integrity checks on structured datasets, producing high-quality, error-reduced inputs intended to improve downstream model and analysis reliability. Developed and maintained documentation for data entry standards to ensure consistent labeling or annotation guidelines across datasets. Worked collaboratively with cross-functional stakeholders to maintain consistency and reduce data quality issues. • Cleaned and validated 10,000+ rows with systematic error checks • Improved accuracy to 99% via validation and quality assurance • Documented data entry standards for guideline adherence • Coordinated across teams to preserve data integrity across datasets

2022 - Present

Meticulous AI Trainer candidate (AI output evaluation, safety/factuality checks, rubric-followed feedback)

Don't discloseText

Served as an AI Trainer candidate focused on assessing and improving model behavior by evaluating outputs for factual accuracy, coherence, and safety in line with alignment goals. Applied instruction-following and rubric adherence principles to guide clear, actionable feedback, including bias and hallucination detection checks. Supported RLHF-style workflows by emphasizing safe, high-quality responses and error pattern recognition to improve dataset and model consistency. • Evaluated responses for factual accuracy and logical consistency • Checked coherence, style consistency (US/UK English), and safety • Detected bias and hallucinations as part of output quality evaluation • Provided rubric-based, actionable feedback to improve future outputs

2022 - Present

Education

Work History