Kyra Wintheiser

Key Skills

Software

Labelbox

Top Subject Matter

Senior AI Trainer & Data Annotation Specialist

Top Data Types

Image

Video

Top Label Types

Bounding Box

Point Key Point

Segmentation

Classification

Tracking

Freelancer Overview

I am a dedicated AI Trainer and Data Annotation Specialist with over five years of hands-on experience in large-scale data labeling, linguistic QA, and multimodal annotation for NLP, computer vision, and audio projects. My background in computational linguistics, paired with advanced expertise in machine learning and LLM evaluation, allows me to deliver high-quality training data across diverse domains, including STEM, social media, and multilingual content. I have led annotation and validation projects using platforms like Scale AI, Remotasks, Appen, and Labelbox, consistently achieving top QA acceptance rates and improving model performance. My strengths include developing annotation guidelines, conducting accuracy audits, and ensuring ethical, bias-mitigated datasets for AI applications. I am skilled in transcription, sentiment analysis, content moderation, and AI response evaluation, and thrive in fast-paced, cross-functional environments where data quality and reliability are paramount.

Labeling Experience

Multimodal AI Training Data Annotation for LLMs

LabelboxVideoBounding BoxClassification

Executed large-scale video annotation and quality assurance projects supporting computer vision and multimodal AI systems. Tasks included object detection, scene classification, emotion recognition, and action tracking across diverse video datasets. Applied bounding boxes, segmentation, and keypoint labeling to improve model recall by 18% and precision in action recognition tasks. Reviewed and labeled audio-visual datasets for speech, background noise, accents, and emotional tone, enhancing ASR (Automatic Speech Recognition) and voice assistant models. Delivered consistent annotation quality with IAA > 0.85, ensuring high inter-annotator agreement and ISO-aligned QA standards. Collaborated with cross-functional QA teams to refine annotation guidelines, reducing systemic errors and boosting annotation consistency by 35%. Demonstrated ability to scale projects from thousands to hundreds of thousands of video frames while maintaining strict accuracy thresholds.

Executed large-scale video annotation and quality assurance projects supporting computer vision and multimodal AI systems. Tasks included object detection, scene classification, emotion recognition, and action tracking across diverse video datasets. Applied bounding boxes, segmentation, and keypoint labeling to improve model recall by 18% and precision in action recognition tasks. Reviewed and labeled audio-visual datasets for speech, background noise, accents, and emotional tone, enhancing ASR (Automatic Speech Recognition) and voice assistant models. Delivered consistent annotation quality with IAA > 0.85, ensuring high inter-annotator agreement and ISO-aligned QA standards. Collaborated with cross-functional QA teams to refine annotation guidelines, reducing systemic errors and boosting annotation consistency by 35%. Demonstrated ability to scale projects from thousands to hundreds of thousands of video frames while maintaining strict accuracy thresholds.

2024

Multimodal AI Training Data Annotation for LLMs

LabelboxImageBounding BoxPoint Key Point

Led a large-scale annotation and QA project supporting LLM fine-tuning and multimodal AI training. Annotated and validated over 3M+ text prompts and AI responses, improving factual accuracy by 25–40%. Delivered 10,000+ hours of audio/video transcription with 98%+ word accuracy, including STEM, conversational, and call-center datasets. Conducted multilingual translation and localization QA across 10+ language pairs, ensuring semantic fidelity and cultural relevance. For computer vision tasks, applied bounding boxes, segmentation, and emotion tagging to video and image datasets, enhancing model recall by 18%. Executed linguistic QA audits with ISO-aligned processes, achieving 99%+ acceptance rates and reducing rework by 30%. This project demonstrates expertise in cross-domain annotation (text, audio, video, image), linguistic QA, and LLM evaluation, showcasing the ability to scale datasets from thousands to millions while maintaining strict quality thresholds.

Led a large-scale annotation and QA project supporting LLM fine-tuning and multimodal AI training. Annotated and validated over 3M+ text prompts and AI responses, improving factual accuracy by 25–40%. Delivered 10,000+ hours of audio/video transcription with 98%+ word accuracy, including STEM, conversational, and call-center datasets. Conducted multilingual translation and localization QA across 10+ language pairs, ensuring semantic fidelity and cultural relevance. For computer vision tasks, applied bounding boxes, segmentation, and emotion tagging to video and image datasets, enhancing model recall by 18%. Executed linguistic QA audits with ISO-aligned processes, achieving 99%+ acceptance rates and reducing rework by 30%. This project demonstrates expertise in cross-domain annotation (text, audio, video, image), linguistic QA, and LLM evaluation, showcasing the ability to scale datasets from thousands to millions while maintaining strict quality thresholds.

2022 - 2024

Education

Work History