Gabriel Garmon

Key Skills

Software

Labelbox

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Image

Audio

Video

Top Label Types

Bounding Box

Polygon

Segmentation

Object Detection

Tracking

Classification

Emotion Recognition

Transcription

Action Recognition

Freelancer Overview

I am an AI Training Specialist with over three years of hands-on experience in data labeling, annotation, and dataset preparation for machine learning and AI projects. My background includes extensive work with image, video, and audio annotation, supporting computer vision workflows such as object detection, tracking, semantic and instance segmentation, and speech transcription. I am proficient in industry-standard tools like Labelbox, CVAT, Supervisely, V7 Darwin, Scale AI, and Amazon SageMaker Ground Truth, and I prepare datasets in COCO and Pascal VOC formats to ensure seamless integration with model training pipelines. My technical skills include Python for data preprocessing and automation, as well as familiarity with OpenCV, JSON, and XML. I excel at maintaining annotation quality and consistency, conducting thorough dataset validation, and collaborating with machine learning engineers to enhance data quality and model performance. I am passionate about delivering precise, high-quality training data and thrive in remote, deadline-driven environments.

Labeling Experience

Video Annotation for Multi-Object Tracking and Action Recognition

LabelboxVideoBounding BoxSegmentation

Currently working on high-volume video annotation projects for AI-based object tracking and action recognition systems. Annotating 200,000+ video frames for multi-object tracking. Performing frame-by-frame bounding box annotation. Labeling object movement patterns and action recognition events. Ensuring temporal consistency across video sequences. Preparing training datasets compatible with YOLO and tracking frameworks. Conducting multi-level QA validation to maintain 98%+ consistency.

Currently working on high-volume video annotation projects for AI-based object tracking and action recognition systems. Annotating 200,000+ video frames for multi-object tracking. Performing frame-by-frame bounding box annotation. Labeling object movement patterns and action recognition events. Ensuring temporal consistency across video sequences. Preparing training datasets compatible with YOLO and tracking frameworks. Conducting multi-level QA validation to maintain 98%+ consistency.

2023

LLM Text Evaluation and AI Response Quality Rating

AppenTextClassificationText Generation

Worked as an AI Quality Analyst on a short-term Large Language Model (LLM) evaluation project focused on improving AI-generated text quality and safety. Reviewed and analyzed thousands of prompt-response pairs, carefully evaluating outputs for accuracy, factual consistency, relevance, coherence, clarity, tone, and compliance with safety policies. Assigned structured classification labels and quality ratings based on detailed annotation guidelines, ensuring consistent evaluation standards across batches. Identified hallucinations, biased responses, harmful content, and logical inconsistencies, providing actionable feedback to support Reinforcement Learning from Human Feedback (RLHF) and fine-tuning processes. Participated in random audits and quality checks to maintain high inter-annotator agreement and labeling accuracy. Successfully evaluated over 5,000+ text samples while meeting strict turnaround deadlines, contributing to measurable improvements in model reliability.

Worked as an AI Quality Analyst on a short-term Large Language Model (LLM) evaluation project focused on improving AI-generated text quality and safety. Reviewed and analyzed thousands of prompt-response pairs, carefully evaluating outputs for accuracy, factual consistency, relevance, coherence, clarity, tone, and compliance with safety policies. Assigned structured classification labels and quality ratings based on detailed annotation guidelines, ensuring consistent evaluation standards across batches. Identified hallucinations, biased responses, harmful content, and logical inconsistencies, providing actionable feedback to support Reinforcement Learning from Human Feedback (RLHF) and fine-tuning processes. Participated in random audits and quality checks to maintain high inter-annotator agreement and labeling accuracy. Successfully evaluated over 5,000+ text samples while meeting strict turnaround deadlines, contributing to measurable improvements in model reliability.

2024 - 2025

Audio Transcription and Speech Data Annotation

LabelboxAudioClassificationEmotion Recognition

Contributed to speech recognition and conversational AI training datasets. Transcribed 3,000+ hours of audio data with high linguistic accuracy. Labeled speaker segments and timestamps. Annotated emotion recognition markers for sentiment analysis. Cleaned and standardized transcripts for model fine-tuning. Conducted QA checks to ensure 99% transcription accuracy. Followed strict formatting and annotation guidelines.

Contributed to speech recognition and conversational AI training datasets. Transcribed 3,000+ hours of audio data with high linguistic accuracy. Labeled speaker segments and timestamps. Annotated emotion recognition markers for sentiment analysis. Cleaned and standardized transcripts for model fine-tuning. Conducted QA checks to ensure 99% transcription accuracy. Followed strict formatting and annotation guidelines.

2023 - 2023

Image Annotation for Object Detection and Classification

LabelboxImageBounding BoxPolygon

Worked on large-scale image annotation projects supporting machine learning and computer vision model development. Annotated 120,000+ images for object detection and classification tasks. Created precise bounding boxes and polygon annotations for multiple object categories. Performed semantic and instance segmentation for advanced AI training. Converted datasets into COCO and YOLO formats. Maintained 98%+ annotation accuracy through structured QA processes. Followed strict annotation taxonomies and labeling guidelines.

Worked on large-scale image annotation projects supporting machine learning and computer vision model development. Annotated 120,000+ images for object detection and classification tasks. Created precise bounding boxes and polygon annotations for multiple object categories. Performed semantic and instance segmentation for advanced AI training. Converted datasets into COCO and YOLO formats. Maintained 98%+ annotation accuracy through structured QA processes. Followed strict annotation taxonomies and labeling guidelines.

2022 - 2022

Education

Work History