Key Skills

Software

Appen

CVAT

Data Annotation Tech

Lionbridge

Remotasks

Scale AI

SuperAnnotate

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Medical Dicom

Video

Top Task Types

Bounding Box

Computer Programming Coding

Emotion Recognition

Evaluation Rating

RLHF

Freelancer Overview

With over nine years of experience in data annotation, AI training data, and linguistic QA, I’ve contributed to high-impact projects with companies like Mindrift, Lionbridge, and Oneforma. My strong foundation in Communication and Media Studies, combined with a Master’s degree in Computer Science with a focus on Human-Computer Interaction, enables me to approach AI training tasks with both technical precision and linguistic sensitivity. I specialize in text data labeling, machine translation post-editing, and quality assurance for NLP models. I’m adept at using tools such as CAT platforms, annotation interfaces, and evaluation frameworks, consistently delivering accurate, high-quality datasets that power AI solutions. My multidisciplinary expertise and attention to linguistic nuance set me apart in the AI training data space.

Labeling Experience

Multilingual Text Classification & AI Training for NLP Systems

AppenTextText GenerationText Summarization

Participated in a large-scale multilingual AI training project focusing on data annotation, text generation, and linguistic QA for NLP models. Tasks included classifying user queries, annotating named entities across multiple languages, and generating or post-editing training data for machine translation engines. Adhered to strict quality guidelines, consistently maintaining accuracy scores above 95%. Collaborated with international teams and reviewed data to ensure cultural and contextual relevance for global deployment. Contributed to training datasets powering virtual assistants and search engines.

Participated in a large-scale multilingual AI training project focusing on data annotation, text generation, and linguistic QA for NLP models. Tasks included classifying user queries, annotating named entities across multiple languages, and generating or post-editing training data for machine translation engines. Adhered to strict quality guidelines, consistently maintaining accuracy scores above 95%. Collaborated with international teams and reviewed data to ensure cultural and contextual relevance for global deployment. Contributed to training datasets powering virtual assistants and search engines.

2019 - 2024

Object Tracking and Event Annotation for Video-Based AI Models

SuperannotateVideoSegmentationRelationship

Worked on a high-volume video annotation project focused on training computer vision models for object tracking and event detection in real-world environments. Tasks included frame-by-frame annotation of vehicles, pedestrians, and dynamic objects using bounding boxes and segmentation tools. Annotated specific actions such as crossing, turning, or stopping for temporal analysis. Ensured labeling accuracy through multi-step QA reviews and adherence to strict annotation guidelines. Helped improve model performance for edge AI systems in autonomous navigation and smart surveillance applications.

Worked on a high-volume video annotation project focused on training computer vision models for object tracking and event detection in real-world environments. Tasks included frame-by-frame annotation of vehicles, pedestrians, and dynamic objects using bounding boxes and segmentation tools. Annotated specific actions such as crossing, turning, or stopping for temporal analysis. Ensured labeling accuracy through multi-step QA reviews and adherence to strict annotation guidelines. Helped improve model performance for edge AI systems in autonomous navigation and smart surveillance applications.

2018 - 2024

Code Annotation and Classification for AI Code Generation Models

SuperannotateComputer Code ProgrammingRLHFFine Tuning

Contributed to training large language models specialized in code generation by annotating and classifying programming snippets in Python, JavaScript, and Java. Tasks included tagging functions by purpose, writing descriptive comments, identifying syntax or logical bugs, and evaluating AI-generated code for correctness and efficiency. Collaborated on iterative QA processes to ensure model training data met technical standards. This project supported the development of intelligent code assistants used by developers in IDEs and documentation tools.

Contributed to training large language models specialized in code generation by annotating and classifying programming snippets in Python, JavaScript, and Java. Tasks included tagging functions by purpose, writing descriptive comments, identifying syntax or logical bugs, and evaluating AI-generated code for correctness and efficiency. Collaborated on iterative QA processes to ensure model training data met technical standards. This project supported the development of intelligent code assistants used by developers in IDEs and documentation tools.

2015 - 2024

Education

Work History