Madeline Lattimore

Key Skills

Software

AWS SageMaker

Appen

CloudFactory

CrowdFlower

CrowdSource

CVAT

Google Cloud Vertex AI

Labelbox

Remotasks

Roboflow

Sama

Scale AI

Clickworker

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Text

Video

Top Task Types

Audio Recording

Data Collection

Emotion Recognition

Evaluation Rating

Fine Tuning

Freelancer Overview

I am an experienced AI data labeler and language model trainer with over three years of hands-on annotation and quality assurance work. My projects span English speech recognition, natural-language understanding, and large language model (LLM) evaluation. I have led complex audio transcription and segmentation tasks, created detailed labeling guidelines, and applied rigorous QA checks to ensure clean, scalable datasets. Drawing on a decade of instructional design and content development experience, I bring advanced skills in organizing information, building repeatable workflows, and documenting edge cases so models learn with precision. I am fluent in key labeling tools and platforms, comfortable with tight deadlines, and known for accuracy and clear communication with cross-functional teams. Whether training cutting-edge conversational AI, refining text-to-speech models, or developing prompt-response evaluation protocols, I combine technical expertise with educator insight to deliver data that improves model performance from day one.

Labeling Experience

Audio Annotation and Elearning Project

ClickworkerVideoFine TuningPrompt Response Writing SFT

Managed an advanced audio annotation project focused on conversational AI and e-learning content. Curated and labeled diverse English recordings for fine-tuning and evaluation tasks, applying text summarization and localization to capture nuances in regional speech. Employed Appen’s platform to tag intent, sentiment, and context while meeting strict turnaround deadlines. Built comprehensive QA protocols—including double-blind reviews and automated consistency checks—that consistently maintained above-99% data integrity. Delivered structured datasets that directly improved speech recognition accuracy and natural language understanding for next-gen learning applications.

Managed an advanced audio annotation project focused on conversational AI and e-learning content. Curated and labeled diverse English recordings for fine-tuning and evaluation tasks, applying text summarization and localization to capture nuances in regional speech. Employed Appen’s platform to tag intent, sentiment, and context while meeting strict turnaround deadlines. Built comprehensive QA protocols—including double-blind reviews and automated consistency checks—that consistently maintained above-99% data integrity. Delivered structured datasets that directly improved speech recognition accuracy and natural language understanding for next-gen learning applications.

2023 - 2024

AI model development in learning and training applications

AppenAudioText SummarizationTranslation Localization

Led a large-scale audio labeling and fine-tuning initiative supporting AI model development in learning and training applications. Performed text summarization, translation/localization, and evaluation/rating of English audio data to ensure accurate context and natural-language flow. Used Appen and complementary QA tools to segment, transcribe, and classify thousands of audio files. Maintained detailed labeling guidelines, documented edge cases, and performed multi-round quality checks to exceed 98% accuracy. Collaborated with cross-functional teams to refine prompts and improve training data efficiency, contributing to faster, more reliable model performance.

Led a large-scale audio labeling and fine-tuning initiative supporting AI model development in learning and training applications. Performed text summarization, translation/localization, and evaluation/rating of English audio data to ensure accurate context and natural-language flow. Used Appen and complementary QA tools to segment, transcribe, and classify thousands of audio files. Maintained detailed labeling guidelines, documented edge cases, and performed multi-round quality checks to exceed 98% accuracy. Collaborated with cross-functional teams to refine prompts and improve training data efficiency, contributing to faster, more reliable model performance.

2024 - 2025

Education

Work History