Abigail N.

Key Skills

Software

Internal/Proprietary Tooling

Other

Top Subject Matter

Artificial Intelligence (AI)

Machine Learning

Natural Language Processing (NLP)

Top Data Types

Text

Audio

Top Task Types

Text Generation

Data Collection

Function Calling

RLHF

Evaluation/Rating

Question Answering

Prompt + Response Writing (SFT)

Fine-tuning

Freelancer Overview

I have experience contributing to AI training and data labeling projects through my work with Outlier,mirco1 ai and workable.com, where I supported the development and improvement of AI models through accurate data annotation, evaluation, and quality-focused task execution. My work involved carefully following project guidelines, reviewing AI-generated outputs, identifying inaccuracies, and ensuring data quality standards were met to improve model performance. Working in this environment strengthened my ability to think critically, maintain consistency, and deliver high-quality work under deadlines. What sets me apart is my strong attention to detail, adaptability, and ability to quickly understand evolving project requirements. Alongside my background in virtual assistance, I bring strong research, communication, and organizational skills that help me work efficiently in remote environments. I am comfortable handling repetitive, detail-sensitive tasks without compromising quality and take pride in producing accurate results that contribute to more reliable and effective AI systems.

Labeling Experience

AI Training Data Annotator

TextText Summarization

Project Scope & Responsibilities Contributed to AI training and data annotation projects focused on improving the accuracy, relevance, and safety of large language model (LLM) outputs. The scope of the project involved reviewing and evaluating AI-generated responses, assessing prompt-response quality, and supporting the refinement of training datasets used to improve model performance. Tasks required close adherence to detailed project instructions, analytical thinking, and consistent decision-making across high-volume assignments. Data Labeling / Annotation Tasks Performed • Evaluated and rated AI-generated responses based on relevance, accuracy, clarity, and instruction-following • Compared multiple model outputs to identify the strongest response based on defined criteria • Labeled and categorized text data according to annotation guidelines • Identified inconsistencies, inaccuracies, or low-quality outputs for quality improvement • Maintained annotation consistency across tasks while adapting to evolving project requirements Project Size & Quality Measures Worked on large-scale, high-volume annotation tasks in a remote environment, managing multiple assignments while maintaining accuracy and efficiency. Adhered strictly to quality assurance standards, including detailed guideline compliance, consistency checks, accuracy reviews, and feedback implementation to ensure high-quality training data and reliable project outcomes.

2026 - 2026

Education

Work History