Ernest T. Gilligaen

Key Skills

Software

Labelbox

Other

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Image

Top Task Types

Evaluation Rating

Computer Programming Coding

Prompt Response Writing SFT

Transcription

Bounding Box

Polygon

Point Key Point

Polyline

Freelancer Overview

I have a diverse background in technical support, market research, and data collection, which has given me a strong foundation in accuracy, attention to detail, and effective communication—key skills for data labeling and AI training data roles. My experience includes recording and verifying household water meter data, conducting telephone-based market and public opinion research, and supporting high-profile clients such as Apple and Mercedes. With a technical skill set spanning Javascript, Python, C++, C#, HTML, and CSS, as well as familiarity with tools like NodeJS and ReactJS, I am comfortable working with both structured and unstructured data. My ongoing studies in computer engineering further enhance my analytical abilities, and I am fluent in Czech, English, Greek, and French, making me well-suited for multilingual data annotation tasks. I am eager to contribute my skills to projects involving data quality, annotation, and the development of AI systems.

Labeling Experience

Remoter

OtherImageBounding BoxPolygon

Labeled and classified objects in images to train and improve computer vision models. Ensured accuracy and consistency across datasets by identifying multiple object types, verifying annotations, and correcting mislabeled items to enhance AI detection performance.

Labeled and classified objects in images to train and improve computer vision models. Ensured accuracy and consistency across datasets by identifying multiple object types, verifying annotations, and correcting mislabeled items to enhance AI detection performance.

2025

Labelbox

LabelboxComputer Code ProgrammingEvaluation RatingComputer Programming Coding

Reviewed AI-generated code outputs from two different models, identified strengths, weaknesses, and errors in both solutions, and provided structured feedback to improve model performance and reliability. Used the Claude command-line tool to troubleshoot GitHub repositories by testing model-generated bug fixes and feature implementations. Compared two proposed code trees, assessed correctness, code quality, and adherence to project requirements.

Reviewed AI-generated code outputs from two different models, identified strengths, weaknesses, and errors in both solutions, and provided structured feedback to improve model performance and reliability. Used the Claude command-line tool to troubleshoot GitHub repositories by testing model-generated bug fixes and feature implementations. Compared two proposed code trees, assessed correctness, code quality, and adherence to project requirements.

2025

Education

Work History