Maxwel O.

Key Skills

Software

CVAT

Remotasks

Scale AI

Labelbox

Surge AI

Other

Top Subject Matter

Industrial Domain Expertise

Manufacturing Domain Expertise

Refinery Image Annotation and QA

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Object Detection

Classification

Freelancer Overview

AI Data Labeling Specialist & Annotation Quality Reviewer. Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include CVAT, Remotasks, and Other. Education includes Bachelor of Science, Zetech University (2023). AI-training focus includes data types such as Image and labeling workflows including Bounding Box, Classification, and Evaluation.

Labeling Experience

AI Data Labeling Specialist & Annotation Quality Reviewer

CVATImageBounding Box

As an AI Data Labeling Specialist & Annotation Quality Reviewer, I performed high-volume image annotation and conducted multi-pass quality assurance across diverse industrial datasets. My work emphasized applying bounding boxes, polygons, and semantic masks and maintaining annotation accuracy above 98.7%. I specialized in reviewing and correcting auto-generated labels and conducting consistency audits to optimize large-scale computer vision projects. • Annotated and quality-reviewed over 18,000 images from industrial and manufacturing datasets using CVAT. • Identified and corrected label errors, including misclassifications, missing objects, and duplication in AI pre-labeled data. • Maintained average annotation throughput of 300–500 frames per session, consistently hitting productivity targets. • Prepared export-ready validated data in multiple formats for AI model training pipelines.

2023 - Present

AI Quality Review & Validation Specialist (Multiple Projects)

Surge AIImage

In the AI Quality Review & Validation Task Suite, I performed structured quality review and error reporting across five concurrent annotation validation projects. My role involved peer review, adjudicating disputed image labels, scoring annotation batches, and maintaining top-tier quality metrics. I was selected for advanced QA reviewer status due to consistent quality output. • Reviewed and scored peer-annotated images using rubric-based platforms. • Generated detailed error reports for feedback improvement. • Maintained platform-leading quality scores (96.8%). • Participated in advanced calibration sessions for annotation consistency.

2022 - 2022

Manufacturing Safety Image Classification Specialist

LabelboxImageClassification

In the Manufacturing Safety Image Classification project, I reviewed and classified more than 6,500 manufacturing facility images for safety compliance. I tagged images for PPE adherence, hazard presence, restricted access, machinery operations, and worker proximity alerts per strict guidelines. My batches achieved a 97.5% classification accuracy score across all reviewed submissions. • Applied safety compliance and operational classification scheme. • Processed images from diverse manufacturing domains (automotive, chemical, food). • Used structured annotation formats for consistency. • Supported workplace safety AI system training.

2022 - 2022

Industrial Equipment Detection Dataset Contributor

Scale AIImageBounding Box

I contributed to constructing a ground truth object detection dataset for industrial equipment recognition, annotating and reviewing over 8,000 images. I applied multi-class bounding box and polygon annotation, performed label consistency audits, and documented edge cases. This dataset supported computer vision models for automated site safety monitoring. • Annotated images with cranes, forklifts, conveyor systems, and safety barriers. • Focused on multi-class label application and consistency audits. • Documented edge cases to inform ontology expansion. • Supported model development for site safety inspection.

2022 - 2022

Image Annotation Specialist — Object Detection & Classification

OtherImageBounding Box

As an Image Annotation Specialist, I contributed to object detection datasets for computer vision model training targeting industrial and manufacturing settings. I performed bounding box, polygon, and point-based annotations, ensuring strict adherence to annotation quality guidelines and zero critical violations. My work supported defect detection and safety compliance AI model development. • Labeled objects across 30+ classes in over 10,000 images, covering varied environments and conditions. • Maintained annotation quality above 98% in all project phases, with minimal rework required. • Rapidly onboarded new annotation domains including aerial and thermal imaging. • Used self-QA checklists to reduce rejection rates and accelerate batch submissions.

2022 - 2022

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Education

Work History