Jennifer Onuoha

Key Skills

Software

Appen

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Text

Video

Top Task Types

Audio Recording

Evaluation/Rating

Prompt + Response Writing (SFT)

Text Generation

Text Summarization

Freelancer Overview

I’m an experienced AI training data specialist with a background in linguistics, media, and multimodal content analysis. Over the past four years, I’ve contributed to NLP and GenAI projects for global clients across audio, video, text, and visual datasets—maintaining 98.9%+ accuracy across complex annotation tasks. My work has involved LLM evaluation, content categorization, sentiment tagging, and QA validation using proprietary tools and platforms like Jira, Confluence, and Google Suite. I bring a hybrid skill set that bridges technical data labeling precision with strong communication, editorial, and project operations expertise gained through roles in tech, media, and enterprise marketing. I thrive in fast-paced environments, collaborating cross-functionally to ensure model training aligns with both linguistic nuance and content standards.

Labeling Experience

Linguist – Voice Characteristics & Speech Delivery Annotation

Internal Proprietary ToolingAudioText GenerationEvaluation Rating

Served as a Linguist for English-language audio recordings, tasked with enhancing AI-driven speech recognition systems. Reviewed and refined pre-labeled data to ensure descriptions accurately captured voice characteristics (pitch, timbre, articulation) and speech delivery (tone shifts, pace, volume). Applied phonetics and linguistic knowledge to annotate voice patterns, maintain idiomatic fluency standards, and ensure alignment with phonological norms. Played a critical role in improving training data quality through precise editorial corrections and linguistic insight, contributing to the development of more naturalistic speech generation models.

Served as a Linguist for English-language audio recordings, tasked with enhancing AI-driven speech recognition systems. Reviewed and refined pre-labeled data to ensure descriptions accurately captured voice characteristics (pitch, timbre, articulation) and speech delivery (tone shifts, pace, volume). Applied phonetics and linguistic knowledge to annotate voice patterns, maintain idiomatic fluency standards, and ensure alignment with phonological norms. Played a critical role in improving training data quality through precise editorial corrections and linguistic insight, contributing to the development of more naturalistic speech generation models.

2024

Multimodal AI Data Annotator & Linguistic Analyst (Appen/Client Projects)

AppenTextClassificationQuestion Answering

Contributed to large-scale AI training projects with Appen for over 3 years, focused on improving LLMs and multimodal models across diverse domains. Tasks included annotating and evaluating data sets in audio, text, and video formats for tone, sentiment, context accuracy, and content relevance. Performed QA and editorial tagging to refine model output for GenAI applications.

Contributed to large-scale AI training projects with Appen for over 3 years, focused on improving LLMs and multimodal models across diverse domains. Tasks included annotating and evaluating data sets in audio, text, and video formats for tone, sentiment, context accuracy, and content relevance. Performed QA and editorial tagging to refine model output for GenAI applications.

2020

Education

Work History