Lauren Mcneil

Key Skills

Software

Mercor

Top Subject Matter

Large Language Models (LLMs) evaluation

RLHF workflows

Comparative judgment

Top Data Types

Text

Image

Video

Top Task Types

Classification

RLHF

Prompt Response Writing SFT

Freelancer Overview

I have precise judgement and experience in data labeling, multimodal evaluation, and AI training data workflows. I have extensive experience in ranking and scoring generative model outputs, auditing responses for accuracy and clarity, and applying rubric-based guidance for consistent high-quality annotations. What sets me apart is my ability to combine analytical reasoning, domain knowledge, and real-world task design. My background in communication-heavy roles also gives me a strong sense for tone and user intent.

Labeling Experience

Audio Model Trainer (Mercor)

MercorImage

Ranked and evaluated multimodal AI outputs, including image, audio, and video responses, against prompts to measure model performance. Applied comparative judgment methods to annotate relevance, coherence, and quality, ensuring robust preference-based reinforcement learning data. Maintained high annotation accuracy across large-scale workflows and collaborated for multimodal capability improvements. • Ranked hundreds of multimodal outputs for quality • Used annotation platforms to label model-generated image, audio, and video content • Delivered actionable annotation-based feedback for model enhancement • Consistently met high standards, earning formal recognition

Ranked and evaluated multimodal AI outputs, including image, audio, and video responses, against prompts to measure model performance. Applied comparative judgment methods to annotate relevance, coherence, and quality, ensuring robust preference-based reinforcement learning data. Maintained high annotation accuracy across large-scale workflows and collaborated for multimodal capability improvements. • Ranked hundreds of multimodal outputs for quality • Used annotation platforms to label model-generated image, audio, and video content • Delivered actionable annotation-based feedback for model enhancement • Consistently met high standards, earning formal recognition

2025 - Present

AI Trainer (Handshake AI Fellowship)

Text

Reviewed and provided feedback on large language model (LLM) outputs to improve AI comprehension of complex conversational interactions. Evaluated and audited scientific accuracy, clarity, and depth in LLM responses using domain-specific checklists, ensuring reliability and quality. Developed and refined prompt outcomes through systematic annotation, contributing to reduced error rates and better model performance. • Used annotation tools to systematically evaluate AI output • Checked scientific accuracy and clarity for subfields using checklists • Audited and refined LLM responses for prompt improvement • Enhanced scientific reliability and reduced error rates

Reviewed and provided feedback on large language model (LLM) outputs to improve AI comprehension of complex conversational interactions. Evaluated and audited scientific accuracy, clarity, and depth in LLM responses using domain-specific checklists, ensuring reliability and quality. Developed and refined prompt outcomes through systematic annotation, contributing to reduced error rates and better model performance. • Used annotation tools to systematically evaluate AI output • Checked scientific accuracy and clarity for subfields using checklists • Audited and refined LLM responses for prompt improvement • Enhanced scientific reliability and reduced error rates

2025 - Present

AI Data Annotator (Welocalize)

TextClassification

Performed high-volume data labeling in multiple languages for AI/ML content using a web-based annotation platform. Ensured precise label verification and cultural relevance by following linguistic guidelines, contributing to high-quality annotated datasets. Communicated project issues and insights through tracking tools to boost process integrity and workflow efficiency. • Conducted text classification and stylistic review tasks • Used annotation software to label content for AI training • Assessed cultural and geopolitical context for content localization • Reported and resolved annotation bugs effectively

Performed high-volume data labeling in multiple languages for AI/ML content using a web-based annotation platform. Ensured precise label verification and cultural relevance by following linguistic guidelines, contributing to high-quality annotated datasets. Communicated project issues and insights through tracking tools to boost process integrity and workflow efficiency. • Conducted text classification and stylistic review tasks • Used annotation software to label content for AI training • Assessed cultural and geopolitical context for content localization • Reported and resolved annotation bugs effectively

2024 - Present

Education

Work History