Geraldine Linus

Key Skills

Software

Lionbridge

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Video

Top Task Types

Entity (NER) Classification

Question Answering

Text Generation

RLHF

Evaluation/Rating

Classification

Object Detection

Action Recognition

Freelancer Overview

I am an experienced AI data annotation contributor with a strong background in labeling and reviewing text, image, audio, and math-based datasets for machine learning projects. My work with Lionbridge AI and TELUS Digital AI has strengthened my ability to follow complex guidelines, maintain high annotation accuracy, and perform detailed quality checks across various data types, including natural language processing and logic-based tasks. With a Bachelor’s degree in Computer Engineering, I bring strong analytical thinking and structured problem-solving skills to AI training workflows. I am highly adaptable to new tools and project requirements, thrive in remote environments, and consistently deliver reliable, error-free results. My attention to detail, strong time management, and commitment to quality make me well-suited for freelance and contract roles focused on data labeling and AI training data.

Labeling Experience

AI Data Annotation & Evaluation Contributor

LionbridgeImageEntity Ner ClassificationQuestion Answering

Contributor working on AI training and evaluation projects through Aurora Studio at Lionbridge AI. Responsible for labeling and reviewing text, image, and audio datasets used in machine learning model development. Tasks include classification, entity recognition (NER), response evaluation, and rating AI-generated outputs for accuracy, relevance, and compliance with detailed project guidelines. Regularly perform structured quality checks to ensure consistency and high annotation accuracy. Evaluate AI responses for logical correctness, instruction adherence, and overall output quality, contributing to the improvement of large language models. Adapt quickly to new task types, evolving guidelines, and performance benchmarks while maintaining productivity in a remote, task-based environment.

Contributor working on AI training and evaluation projects through Aurora Studio at Lionbridge AI. Responsible for labeling and reviewing text, image, and audio datasets used in machine learning model development. Tasks include classification, entity recognition (NER), response evaluation, and rating AI-generated outputs for accuracy, relevance, and compliance with detailed project guidelines. Regularly perform structured quality checks to ensure consistency and high annotation accuracy. Evaluate AI responses for logical correctness, instruction adherence, and overall output quality, contributing to the improvement of large language models. Adapt quickly to new task types, evolving guidelines, and performance benchmarks while maintaining productivity in a remote, task-based environment.

2025 - 2025

AI Data Annotation & Evaluation Contributor

TelusImageClassificationObject Detection

Contributor on computer vision projects involving both image and video datasets for machine learning model training and evaluation. Responsible for classifying images and video content according to detailed annotation guidelines, identifying objects, actions, and contextual elements to ensure high-quality labeled data. Performed structured quality checks to maintain consistency and minimize errors across large datasets. Evaluated outputs for accuracy, completeness, and compliance with project standards while meeting defined productivity benchmarks. Demonstrated strong attention to detail and the ability to apply complex classification criteria reliably in a remote work environment.

Contributor on computer vision projects involving both image and video datasets for machine learning model training and evaluation. Responsible for classifying images and video content according to detailed annotation guidelines, identifying objects, actions, and contextual elements to ensure high-quality labeled data. Performed structured quality checks to maintain consistency and minimize errors across large datasets. Evaluated outputs for accuracy, completeness, and compliance with project standards while meeting defined productivity benchmarks. Demonstrated strong attention to detail and the ability to apply complex classification criteria reliably in a remote work environment.

2024 - 2025

Education

Work History