Adeyinka Adekanmi

Key Skills

Software

Mercor

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Task Types

RLHF

Evaluation/Rating

Prompt + Response Writing (SFT)

Freelancer Overview

I am an experienced data annotation expert with hands-on experience in delivering high-quality labeling and structured data for AI training pipelines, particularly in natural language understanding, reasoning, and instruction-following models. My background includes evaluating and categorizing AI model outputs for relevance and coherence across diverse domains such as education, engineering, healthcare, and financial services. I am skilled in collaborating with AI trainers and engineers to refine annotation guidelines and address edge cases, ensuring consistency and accuracy. Proficient in tools like Python, R Studio, MATLAB, and data analysis platforms, I bring a strong foundation in technical writing, critical assessment, and the ability to adapt quickly to new annotation standards and project requirements. My multidisciplinary research and teaching experience further enhance my attention to detail and understanding of complex data structures, making me well-suited for roles in data labeling and AI training data.

Labeling Experience

Digital Annotation Expert

MercorTextRLHFEvaluation Rating

• Delivered high quality annotations and structured data labeling across diverse AI training pipelines, with a focus on natural language understanding, reasoning, and instruction following models. • Evaluated and categorized AI model outputs for relevance, coherence, and alignment with task objectives in domains such as education, engineering, healthcare, and financial services. • Collaborated asynchronously with AI trainers and engineers to ensure annotation guidelines were consistently applied and contributed to refining labeling instructions for edge-case handling.

• Delivered high quality annotations and structured data labeling across diverse AI training pipelines, with a focus on natural language understanding, reasoning, and instruction following models. • Evaluated and categorized AI model outputs for relevance, coherence, and alignment with task objectives in domains such as education, engineering, healthcare, and financial services. • Collaborated asynchronously with AI trainers and engineers to ensure annotation guidelines were consistently applied and contributed to refining labeling instructions for edge-case handling.

2025 - 2025

Education

Work History