Tommy Castol

Key Skills

Software

Appen

CVAT

LabelImg

Labelbox

Label Studio

Mercor

Roboflow

Scale AI

Telus

OneForma

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Text

Top Label Types

Bounding Box

Point Key Point

Segmentation

Classification

Tracking

Polygon

Object Detection

Evaluation Rating

Data Collection

Prompt Response Writing SFT

Freelancer Overview

I am an experienced AI Training Specialist with a strong background in data labeling and annotation for image, video, and audio datasets. Over the past three years, I have developed expertise in object detection, segmentation, keypoint annotation, and audio transcription, working extensively with industry-standard tools like Labelbox, YOLO, CVAT, and Roboflow. My work has focused on preparing high-quality datasets in formats such as COCO and Pascal VOC, ensuring annotation accuracy through rigorous QA processes, and collaborating closely with machine learning engineers to refine guidelines and improve model performance. I am highly detail-oriented, accuracy-driven, and skilled in both computer vision and audio classification projects, always striving to deliver precise and reliable training data that enhances AI systems.

Labeling Experience

Text Annotation and NLP Dataset Labeling for Large Language Models

TextClassification

Worked on AI training projects focused on large-scale text dataset annotation to support the development of natural language processing (NLP) and large language models (LLMs). Responsible for labeling and classifying text data for tasks such as Named Entity Recognition (NER), sentiment analysis, intent classification, and topic categorization. Performed prompt and response writing for supervised fine-tuning (SFT) datasets, evaluated AI-generated outputs, and rated model responses based on accuracy, relevance, safety, and coherence. Contributed to question-answering and text summarization datasets to improve model understanding and reasoning capabilities. Processed and reviewed thousands of text samples while following detailed annotation guidelines to ensure consistency and high-quality outputs. Maintained strict quality assurance standards through review cycles, error detection, and adherence to project requirements, helping improve overall dataset reliability and machine learning model performance.

Worked on AI training projects focused on large-scale text dataset annotation to support the development of natural language processing (NLP) and large language models (LLMs). Responsible for labeling and classifying text data for tasks such as Named Entity Recognition (NER), sentiment analysis, intent classification, and topic categorization. Performed prompt and response writing for supervised fine-tuning (SFT) datasets, evaluated AI-generated outputs, and rated model responses based on accuracy, relevance, safety, and coherence. Contributed to question-answering and text summarization datasets to improve model understanding and reasoning capabilities. Processed and reviewed thousands of text samples while following detailed annotation guidelines to ensure consistency and high-quality outputs. Maintained strict quality assurance standards through review cycles, error detection, and adherence to project requirements, helping improve overall dataset reliability and machine learning model performance.

2025 - Present

Large-Scale Image & Video Annotation for Computer Vision Models

VideoBounding Box

Worked on multiple AI training projects involving large-scale image, video, and audio datasets used for machine learning and computer vision model development. Responsible for annotating thousands of images and video frames using bounding boxes, polygons, segmentation masks, and keypoints to identify and track objects accurately. Performed frame-by-frame video annotation for object tracking and action recognition tasks while ensuring temporal consistency across frames. Conducted audio transcription and classification tasks to support speech recognition and NLP models. Prepared datasets in industry-standard formats such as YOLO, COCO, and Pascal VOC to ensure compatibility with machine learning pipelines. Maintained strict adherence to annotation guidelines and performed quality assurance checks to achieve high accuracy and consistency across datasets.

Worked on multiple AI training projects involving large-scale image, video, and audio datasets used for machine learning and computer vision model development. Responsible for annotating thousands of images and video frames using bounding boxes, polygons, segmentation masks, and keypoints to identify and track objects accurately. Performed frame-by-frame video annotation for object tracking and action recognition tasks while ensuring temporal consistency across frames. Conducted audio transcription and classification tasks to support speech recognition and NLP models. Prepared datasets in industry-standard formats such as YOLO, COCO, and Pascal VOC to ensure compatibility with machine learning pipelines. Maintained strict adherence to annotation guidelines and performed quality assurance checks to achieve high accuracy and consistency across datasets.

2024 - 2025

Computer Vision Data Labeling for Object Detection & Tracking

LabelboxImageBounding Box

Worked as an AI Training Specialist on multiple large-scale computer vision and audio annotation projects focused on improving machine learning model accuracy. Annotated thousands of images and video frames using bounding boxes, polygons, segmentation masks, and keypoints for object detection, tracking, and scene understanding tasks. Performed frame-by-frame video annotation to track moving objects across sequences and prepared datasets in YOLO, COCO, and Pascal VOC formats. Conducted audio transcription and classification tasks with strict accuracy requirements. Maintained high-quality standards by following detailed annotation guidelines, performing QA checks, and correcting inconsistencies. Collaborated with ML engineers to refine labeling strategies, reduce annotation errors, and optimize datasets for training and evaluation.

Worked as an AI Training Specialist on multiple large-scale computer vision and audio annotation projects focused on improving machine learning model accuracy. Annotated thousands of images and video frames using bounding boxes, polygons, segmentation masks, and keypoints for object detection, tracking, and scene understanding tasks. Performed frame-by-frame video annotation to track moving objects across sequences and prepared datasets in YOLO, COCO, and Pascal VOC formats. Conducted audio transcription and classification tasks with strict accuracy requirements. Maintained high-quality standards by following detailed annotation guidelines, performing QA checks, and correcting inconsistencies. Collaborated with ML engineers to refine labeling strategies, reduce annotation errors, and optimize datasets for training and evaluation.

2023 - 2024

Education

Work History