Mercy N.

Key Skills

Software

Data Annotation Tech

Clickworker

Axiom AI

Top Subject Matter

Computer Science / Software Development (Primary)

AI Safety & Evaluation / RLHF (Secondary)

Writing, Editing & Linguistics (Tertiary)

Top Data Types

Text

Top Task Types

Object Detection

Text Generation

Question Answering

Text Summarization

RLHF

Prompt + Response Writing (SFT)

Freelancer Overview

With a strong academic background in Computer Science and extensive hands-on experience in software development, technical editing, and digital media management, I bring a unique, multi-disciplinary skillset to AI training data and data labeling. My experience includes engineering web applications across various stacks (including React, Python, PHP, and WebSockets), which enables me to perform complex code generation evaluations, isolate subtle bugs, and conduct precise technical fact-checking. Complementing my engineering background is my multi-year tenure as an editor for Presby Pearl, where I mastered the art of semantic analysis, strict style-guide adherence, and content verification. Furthermore, my background in managing content pipelines and tracking metrics as a social media specialist has refined my data synthesis and content moderation capabilities. This rare blend of technical proficiency and linguistic precision allows me to deliver high-quality, reliable labeling for both algorithmic logic and complex text-based datasets. What truly sets me apart is my rigorous attention to detail, my active involvement in developer communities like Google Developer Groups (GDG), and my deep commitment to maintaining dataset integrity. Having built real-time communication systems and secure databases, I intimately understand the edge cases and constraints that language models must learn to navigate. I approach data labeling with the analytical mindset of a developer and the structural precision of a professional editor. Above all, I possess a high technical aptitude and a strong willingness to learn on the job, allowing me to master internal platform tooling, adapt to evolving annotation guidelines, and tackle complex, multi-modal workflows swiftly and effectively.

Education

Work History