Dan Cornelius

Key Skills

Software

Appen

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Entity Ner Classification

Freelancer Overview

I am a detail-oriented data analyst with a strong focus on AI training data, data annotation, and model evaluation. My experience includes performing text and dataset annotation for supervised machine learning tasks, evaluating AI-generated responses for accuracy and coherence, and designing structured prompts to optimize large language model outputs. I am skilled in Python, SQL, Excel, and various data labeling tools, and have developed automated reporting solutions that improve efficiency. My background in statistical analysis and research methods enables me to deliver high-quality, reliable datasets that enhance AI system performance. I am passionate about leveraging data-driven insights to improve AI models and am committed to ensuring the integrity and quality of training data in every project I undertake.

Labeling Experience

Computer Vision Image Annotation Specialist

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Worked on large-scale computer vision annotation projects focused on object detection and image classification for machine learning model training. Responsibilities included: Annotated images using bounding boxes and polygon segmentation techniques. Labeled objects such as vehicles, pedestrians, traffic signs, retail products, and infrastructure elements. Performed semantic segmentation for scene understanding tasks. Conducted image classification based on predefined taxonomy guidelines. Reviewed and corrected peer annotations to ensure dataset accuracy and consistency. Maintained strict compliance with annotation guidelines and quality metrics. Utilized QA review cycles to achieve high inter-annotator agreement scores. Project Scope: Annotated and validated over 8,000+ images across transportation and retail datasets.

Worked on large-scale computer vision annotation projects focused on object detection and image classification for machine learning model training. Responsibilities included: Annotated images using bounding boxes and polygon segmentation techniques. Labeled objects such as vehicles, pedestrians, traffic signs, retail products, and infrastructure elements. Performed semantic segmentation for scene understanding tasks. Conducted image classification based on predefined taxonomy guidelines. Reviewed and corrected peer annotations to ensure dataset accuracy and consistency. Maintained strict compliance with annotation guidelines and quality metrics. Utilized QA review cycles to achieve high inter-annotator agreement scores. Project Scope: Annotated and validated over 8,000+ images across transportation and retail datasets.

2024 - 2025

LLM Response Evaluation & Text Annotation Specialist

AppenTextEntity Ner Classification

Worked on AI training and evaluation projects focused on improving Large Language Model (LLM) performance through high-quality data annotation and reinforcement learning from human feedback (RLHF). Key responsibilities included: Evaluating AI-generated responses for accuracy, relevance, factual correctness, tone, and instruction adherence. Rating model outputs using structured evaluation rubrics. Performing Named Entity Recognition (NER) tagging for structured text datasets. Writing and refining prompts to improve response clarity and reduce hallucinations. Comparing multiple model outputs and selecting the most aligned response. Conducting quality assurance reviews to ensure annotation consistency and guideline compliance. Managing datasets using Excel and Google Sheets for tracking performance metrics and reviewer feedback. Project size: Annotated and evaluated over 5,000+ text samples across diverse domains including general knowledge, business, and technical writing

Worked on AI training and evaluation projects focused on improving Large Language Model (LLM) performance through high-quality data annotation and reinforcement learning from human feedback (RLHF). Key responsibilities included: Evaluating AI-generated responses for accuracy, relevance, factual correctness, tone, and instruction adherence. Rating model outputs using structured evaluation rubrics. Performing Named Entity Recognition (NER) tagging for structured text datasets. Writing and refining prompts to improve response clarity and reduce hallucinations. Comparing multiple model outputs and selecting the most aligned response. Conducting quality assurance reviews to ensure annotation consistency and guideline compliance. Managing datasets using Excel and Google Sheets for tracking performance metrics and reviewer feedback. Project size: Annotated and evaluated over 5,000+ text samples across diverse domains including general knowledge, business, and technical writing

2024 - 2025

Education

Work History