Warren Frank

Key Skills

Software

Label Studio

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Text

Image

Top Label Types

Prompt Response Writing SFT

Entity Ner Classification

Classification

Text Generation

Tracking

RLHF

Bounding Box

Evaluation Rating

Freelancer Overview

I am an experienced AI Trainer and Data Annotation Specialist with over three years supporting machine learning and large language model development. My background includes hands-on expertise in dataset annotation, AI response evaluation, reinforcement learning from human feedback (RLHF), and prompt engineering. I have worked extensively with diverse data types—including text, image, audio, and video—using industry-standard tools like Labelbox, Label Studio, Amazon SageMaker Ground Truth, Prodigy, CVAT, and LabelImg. I am skilled in preparing and validating high-quality datasets for NLP and computer vision projects, performing data quality assurance, and designing evaluation workflows to ensure reliable model training. My experience also includes red teaming, AI safety evaluation, and collaborating with teams to optimize data pipelines for model performance and reliability.

Labeling Experience

Data Annotation Specialist – Remote AI Training Projects

Label StudioTextPrompt Response Writing SFT

As a Data Annotation Specialist for remote AI training projects, I am responsible for annotating and preparing datasets for machine learning models. My tasks includes prompt evaluation, RLHF, entity recognition, and classification for both NLP and computer vision datasets. I maintain high standards of accuracy and ensure data consistency across labeling in the project. • Annotating text datasets for prompt + response workflows, RLHF, and entity recognition. • Labeling images with bounding boxes and performed object detection tasks. • Maintaining data quality by reviewing and correcting inconsistencies in labeled datasets. • Using industry standard tools such as Label Studio.

As a Data Annotation Specialist for remote AI training projects, I am responsible for annotating and preparing datasets for machine learning models. My tasks includes prompt evaluation, RLHF, entity recognition, and classification for both NLP and computer vision datasets. I maintain high standards of accuracy and ensure data consistency across labeling in the project. • Annotating text datasets for prompt + response workflows, RLHF, and entity recognition. • Labeling images with bounding boxes and performed object detection tasks. • Maintaining data quality by reviewing and correcting inconsistencies in labeled datasets. • Using industry standard tools such as Label Studio.

2025 - 2024

Computer Vision Annotation Project – Object Detection, Classification & Tracking

Label StudioImageBounding BoxClassification

In this computer vision annotation project, I contributed to training AI models by performing high-quality image and video labeling tasks for object detection and classification datasets. My responsibilities included drawing precise bounding boxes around objects, categorizing labeled items into predefined classes, and tracking object movement across sequential frames in video datasets. The annotations were used to improve machine learning models for real-world applications such as autonomous driving, surveillance systems, and smart city technologies. I ensured consistency and accuracy by following detailed annotation guidelines and performing quality control checks before dataset submission. Key Contributions • Annotated images using bounding boxes for object detection datasets • Performed multi-class object classification for AI training data • Conducted object tracking across video frames to support motion detection models • Maintained high annotation accuracy following strict lab

In this computer vision annotation project, I contributed to training AI models by performing high-quality image and video labeling tasks for object detection and classification datasets. My responsibilities included drawing precise bounding boxes around objects, categorizing labeled items into predefined classes, and tracking object movement across sequential frames in video datasets. The annotations were used to improve machine learning models for real-world applications such as autonomous driving, surveillance systems, and smart city technologies. I ensured consistency and accuracy by following detailed annotation guidelines and performing quality control checks before dataset submission. Key Contributions • Annotated images using bounding boxes for object detection datasets • Performed multi-class object classification for AI training data • Conducted object tracking across video frames to support motion detection models • Maintained high annotation accuracy following strict lab

2025

Data Analyst Assistant – Machine Learning Dataset Preparation

LabelboxTextEntity Ner ClassificationClassification

As a Data Analyst Assistant, I contributed to machine learning dataset preparation by assisting in labeling and validation tasks. I helped maintain organized datasets essential for effective AI model training and documentation of dataset workflows. My role focused on supporting annotation teams to ensure high-quality, well-prepared datasets. • Performed text data labeling and classification tasks for machine learning workflows. • Assisted in text dataset validation and organization within data pipelines. • Documented data preparation procedures to facilitate smooth annotation processes. • Supported quality assurance measures for labeled text data used in AI projects.

As a Data Analyst Assistant, I contributed to machine learning dataset preparation by assisting in labeling and validation tasks. I helped maintain organized datasets essential for effective AI model training and documentation of dataset workflows. My role focused on supporting annotation teams to ensure high-quality, well-prepared datasets. • Performed text data labeling and classification tasks for machine learning workflows. • Assisted in text dataset validation and organization within data pipelines. • Documented data preparation procedures to facilitate smooth annotation processes. • Supported quality assurance measures for labeled text data used in AI projects.

2021 - 2022

Education

Work History