Andrew Danks

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Video

Top Label Types

Bounding Box

Emotion Recognition

Entity Ner Classification

Evaluation Rating

Object Detection

Point Key Point

Prompt Response Writing SFT

Question Answering

Transcription

Freelancer Overview

I am an entry-level AI professional with hands-on experience as a Data Annotator on Outlier’s Aether Generalist project, where I evaluated AI responses, identified errors, ranked outputs, and checked instruction adherence to improve model safety and quality. My work involved applying detailed rubrics and annotation guidelines to both AI- and human-generated content, contributing high-quality training data to large language models in the NLP domain. I bring strong attention to detail, a commitment to AI safety, and a genuine interest in making AI systems more accurate and helpful. I am eager to continue developing my skills in data labeling, model evaluation, and AI quality assurance roles.

Labeling Experience

Data Annotator – Generalist AI Trainer (Outlier – Aether Project)

Scale AITextEvaluation Rating

As a Data Annotator for the Outlier Aether Generalist project, I evaluated and improved AI-generated textual responses. My responsibilities included applying rubrics to critique, rank, and annotate AI versus human content. I helped enhance the accuracy, reasoning, and safety of large language models. • Executed comprehensive reviews of AI model outputs for instruction-following and quality • Identified common errors and provided structured feedback on AI performance • Ranked responses and documented inconsistencies for further model training • Contributed to the development of high-quality training data for frontier large language models

As a Data Annotator for the Outlier Aether Generalist project, I evaluated and improved AI-generated textual responses. My responsibilities included applying rubrics to critique, rank, and annotate AI versus human content. I helped enhance the accuracy, reasoning, and safety of large language models. • Executed comprehensive reviews of AI model outputs for instruction-following and quality • Identified common errors and provided structured feedback on AI performance • Ranked responses and documented inconsistencies for further model training • Contributed to the development of high-quality training data for frontier large language models

2025

Education

Work History