Jonathan Woodruff

Key Skills

Software

Labelbox

Top Subject Matter

Computer Vision

Nlp Domain Expertise

Autonomous Systems

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Classification

Freelancer Overview

AI Training & Data Annotation Specialist. Core strengths include Labelbox. Education includes Bachelor of Science, New York University (2021). AI-training focus includes data types such as Image and Text and labeling workflows including Bounding Box and Classification.

Labeling Experience

Code Annotation & Evaluation for Machine Learning Model Training

Computer Code ProgrammingComputer Programming Coding

Worked on code annotation and evaluation tasks to support the training of machine learning models focused on code understanding, generation, and error detection. The project involved reviewing and labeling code snippets across multiple programming languages, including Python, JavaScript, and SQL. Performed detailed annotation of code datasets by identifying functionality, classifying code intent, and tagging components such as functions, variables, and logic structures. Evaluated code quality based on correctness, efficiency, readability, and adherence to best practices. Handled tasks such as bug detection, code correction, and classification of programming patterns to improve model accuracy in generating and interpreting code. Applied strict annotation guidelines to ensure consistency across datasets and handled edge cases involving ambiguous or incomplete code. Contributed to dataset validation by reviewing annotations, identifying inconsistencies, and providing structured feedback to improve labeling standards. The annotated datasets were used to train and benchmark AI models for code generation and automated programming assistance.

Worked on code annotation and evaluation tasks to support the training of machine learning models focused on code understanding, generation, and error detection. The project involved reviewing and labeling code snippets across multiple programming languages, including Python, JavaScript, and SQL. Performed detailed annotation of code datasets by identifying functionality, classifying code intent, and tagging components such as functions, variables, and logic structures. Evaluated code quality based on correctness, efficiency, readability, and adherence to best practices. Handled tasks such as bug detection, code correction, and classification of programming patterns to improve model accuracy in generating and interpreting code. Applied strict annotation guidelines to ensure consistency across datasets and handled edge cases involving ambiguous or incomplete code. Contributed to dataset validation by reviewing annotations, identifying inconsistencies, and providing structured feedback to improve labeling standards. The annotated datasets were used to train and benchmark AI models for code generation and automated programming assistance.

2025 - Present

AI Training & Data Annotation Specialist

LabelboxImageBounding Box

As an AI Training & Data Annotation Specialist, I supported ML teams in preparing high-quality labeled datasets for computer vision and NLP model training. I was responsible for annotating large sets of images, videos, and audio, using bounding boxes, polygons, segmentation masks, and object tracking for object detection and video intelligence models. I also provided quality control and structured feedback to ensure annotation accuracy, dataset consistency, and guideline compliance. • Annotated 150,000+ images and video frames for computer vision training. • Maintained more than 98% labeling accuracy across multiple projects. • Labeled datasets for autonomous system and surveillance model training. • Conducted dataset validation and error detection to improve model accuracy.

As an AI Training & Data Annotation Specialist, I supported ML teams in preparing high-quality labeled datasets for computer vision and NLP model training. I was responsible for annotating large sets of images, videos, and audio, using bounding boxes, polygons, segmentation masks, and object tracking for object detection and video intelligence models. I also provided quality control and structured feedback to ensure annotation accuracy, dataset consistency, and guideline compliance. • Annotated 150,000+ images and video frames for computer vision training. • Maintained more than 98% labeling accuracy across multiple projects. • Labeled datasets for autonomous system and surveillance model training. • Conducted dataset validation and error detection to improve model accuracy.

2023 - Present

Speech & Audio Annotation for AI Model Trainin

AudioTranscription

Contributed to audio annotation projects focused on training speech recognition and audio classification models. Performed accurate speech-to-text transcription on diverse audio datasets, including conversations, recordings, and real-world audio samples. Labeled and categorized audio data by identifying speech patterns, speaker segments, and environmental sounds. Applied consistent transcription standards, including punctuation, formatting, and speaker identification, to ensure high-quality outputs suitable for model training. Handled audio datasets with varying quality levels, including background noise and overlapping speech, ensuring clarity and accuracy in transcription. Conducted quality checks and reviews to maintain high annotation standards and minimize errors. Supported dataset validation processes by identifying inconsistencies and improving annotation guidelines for better model performance.

Contributed to audio annotation projects focused on training speech recognition and audio classification models. Performed accurate speech-to-text transcription on diverse audio datasets, including conversations, recordings, and real-world audio samples. Labeled and categorized audio data by identifying speech patterns, speaker segments, and environmental sounds. Applied consistent transcription standards, including punctuation, formatting, and speaker identification, to ensure high-quality outputs suitable for model training. Handled audio datasets with varying quality levels, including background noise and overlapping speech, ensuring clarity and accuracy in transcription. Conducted quality checks and reviews to maintain high annotation standards and minimize errors. Supported dataset validation processes by identifying inconsistencies and improving annotation guidelines for better model performance.

2023 - 2023

High-accuracy Text Annotation for NLP Model Training

TextText Generation

Worked on large-scale text annotation projects supporting the development of NLP models for sentiment analysis, intent classification, and text categorization. Labeled diverse text datasets including customer feedback, chat conversations, and short-form content to train machine learning models for language understanding tasks. Applied detailed annotation guidelines to classify sentiment (positive, negative, neutral) and identify user intent across various domains. Handled ambiguous and context-dependent data by ensuring consistency in labeling decisions and aligning with project-specific taxonomy. Processed high-volume datasets consisting of thousands of text entries, ensuring structured and accurate labeling. Collaborated with QA teams to perform validation checks and resolve discrepancies, maintaining high annotation quality and consistency across datasets. Contributed to improving model performance by identifying labeling inconsistencies and providing feedback on guideline clarity and edge cases.

Worked on large-scale text annotation projects supporting the development of NLP models for sentiment analysis, intent classification, and text categorization. Labeled diverse text datasets including customer feedback, chat conversations, and short-form content to train machine learning models for language understanding tasks. Applied detailed annotation guidelines to classify sentiment (positive, negative, neutral) and identify user intent across various domains. Handled ambiguous and context-dependent data by ensuring consistency in labeling decisions and aligning with project-specific taxonomy. Processed high-volume datasets consisting of thousands of text entries, ensuring structured and accurate labeling. Collaborated with QA teams to perform validation checks and resolve discrepancies, maintaining high annotation quality and consistency across datasets. Contributed to improving model performance by identifying labeling inconsistencies and providing feedback on guideline clarity and edge cases.

2023 - 2023

High-accuracy Video Annotation & Object Tracking for Computer Vision Models

VideoObject Detection

Contributed to a large-scale video annotation project focused on training computer vision models for object detection and multi-object tracking in dynamic environments. The project involved annotating thousands of video sequences by tracking objects frame-by-frame using bounding boxes, ensuring temporal consistency across frames. Performed detailed object tracking across complex scenarios, including occlusion, fast motion, and overlapping objects. Maintained consistent object IDs throughout video sequences to support accurate training of tracking algorithms. Applied strict annotation guidelines to ensure uniform labeling across all datasets. Handled high-volume datasets comprising tens of thousands of video frames, contributing to the development of robust video intelligence models. Conducted quality assurance checks, including self-review and guideline validation, to maintain over 98% annotation accuracy. Provided feedback on ambiguous cases and edge scenarios to improve annotation guidelines and dataset consistency. The annotated data was used to enhance model performance in real-time object detection and tracking applications.

Contributed to a large-scale video annotation project focused on training computer vision models for object detection and multi-object tracking in dynamic environments. The project involved annotating thousands of video sequences by tracking objects frame-by-frame using bounding boxes, ensuring temporal consistency across frames. Performed detailed object tracking across complex scenarios, including occlusion, fast motion, and overlapping objects. Maintained consistent object IDs throughout video sequences to support accurate training of tracking algorithms. Applied strict annotation guidelines to ensure uniform labeling across all datasets. Handled high-volume datasets comprising tens of thousands of video frames, contributing to the development of robust video intelligence models. Conducted quality assurance checks, including self-review and guideline validation, to maintain over 98% annotation accuracy. Provided feedback on ambiguous cases and edge scenarios to improve annotation guidelines and dataset consistency. The annotated data was used to enhance model performance in real-time object detection and tracking applications.

2022 - 2023

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Education

Work History