Gain J.

Key Skills

Software

Label Studio

Labelbox

Scale AI

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Text

Computer Code Programming

Top Task Types

Evaluation/Rating

Computer Programming/Coding

Prompt + Response Writing (SFT)

Freelancer Overview

I am an AI training and data annotation specialist with hands-on experience generating high-quality training data for large language models. On Outlier's Aether project I produced supervised fine-tuning (SFT) data in the software engineering and coding domain, writing and reviewing coding prompts and model responses, evaluating code correctness and reasoning quality, and giving detailed feedback to improve model outputs. On Outlier's P2P (prompt-and-response) projects I created and reviewed prompt/response pairs, ranked and rated model outputs, and worked closely to detailed quality rubrics across general-knowledge and reasoning topics. My background as a full-stack software engineer (6+ years across JavaScript/TypeScript, React, Node.js, PHP and Python) strengthens my coding-data work, letting me assess technical correctness and reasoning with precision. I am detail-oriented, consistent with project guidelines, and focused on accuracy, helpfulness, and clear formatting in every submission.

Labeling Experience

Outlier - P2P Projects (Prompt & Response Data)

TextPrompt + Response Writing (SFT)

Worked on Outlier's P2P (prompt-and-response) projects, creating and reviewing prompt/response pairs used to train and fine-tune large language models. Tasks included writing high-quality prompts, authoring or editing model responses, comparing and ranking responses, and rating outputs against detailed quality guidelines. Maintained consistency with project rubrics and incorporated reviewer feedback to ensure accuracy, helpfulness, and adherence to formatting requirements.

2025 - 2026

Outlier - Aether Project (LLM Coding & Reasoning Data)

Computer Code ProgrammingPrompt + Response Writing (SFT)

Contributed to Outlier's Aether project, producing high-quality supervised fine-tuning (SFT) data for large language model training in the software engineering and coding domain. Work involved writing and reviewing coding prompts and model responses, evaluating code correctness and reasoning quality, and providing detailed feedback to improve model outputs. Adhered to strict project guidelines and quality rubrics, with submissions reviewed for accuracy, completeness, and adherence to formatting standards.

2025 - 2026