Mary M.

Key Skills

Software

CVAT

Doccano

Labelbox

Label Studio

Remotasks

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Text

Top Task Types

Evaluation/Rating

Prompt + Response Writing (SFT)

Text Generation

Freelancer Overview

An AI Training & Content Evaluation Specialist with hands-on experience creating, refining, and evaluating training data for text-based AI systems. I excel at applying complex guidelines, fact-checking content, and ensuring logical consistency, clarity, and accuracy in AI outputs. My work has strengthened AI reasoning, text generation quality, and context awareness across multiple domains. I bring strong skills in prompt and response writing (SFT), text summarization, question answering, and evaluation/rating of AI outputs. Highly adaptable, detail-oriented, and proficient in English (C2), I thrive on projects that require precision, analytical thinking, and collaboration with QA teams to produce high-quality, reliable AI training data.

Labeling Experience

AI Text Generation & Evaluation Project

LabelboxTextText GenerationText Summarization

Led the annotation and evaluation of AI-generated text data for LLM training projects. Tasks included creating structured prompts and responses, summarizing documents, labeling entities, and evaluating AI outputs for clarity, factual accuracy, and logical coherence. Collaborated with QA specialists to ensure consistency, adherence to guidelines, and high-quality data for model fine-tuning. Oversaw the annotation of over 5,000 text samples, providing detailed feedback that improved model reasoning and contextual understanding.

Led the annotation and evaluation of AI-generated text data for LLM training projects. Tasks included creating structured prompts and responses, summarizing documents, labeling entities, and evaluating AI outputs for clarity, factual accuracy, and logical coherence. Collaborated with QA specialists to ensure consistency, adherence to guidelines, and high-quality data for model fine-tuning. Oversaw the annotation of over 5,000 text samples, providing detailed feedback that improved model reasoning and contextual understanding.

2023

Education

Work History