Amine Zinoun

Key Skills

Software

AWS SageMaker

Anno-Mage

Appen

Argilla

Axiom AI

Clickworker

CloudFactory

CrowdFlower

CrowdSource

CVAT

Data Annotation Tech

Dataloop

Datatroniq

Datumbox

Datasaur

Datature

Dataturk

Deep Systems

Encord

Figure Eight

Google Cloud Vertex AI

Hasty

Img Lab

Kili Technology

Labelbox

Label Studio

Mighty AI

Mindrift

OneForma

OpenCV AI Kit (OAK)

Snorkel AI

Toloka

Telus

Trilldata Technologies

V7 Labs

Remotasks

Scale AI

Other

Top Subject Matter

Mathematics Domain Expertise

Physics Domain Expertise

Computer Science

Top Data Types

Computer Code Programming

Document

Image

Text

Top Label Types

Classification

Computer Programming Coding

Data Collection

Text Generation

Text Summarization

Freelancer Overview

AI Data Reviewer and Annotator — Outlier AI. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Master of Science, Sultan Moulay Slimane University (2025). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

AI Data Reviewer and Annotator — Outlier AI

OtherText

I served as an AI Data Reviewer and Annotator, focusing on reviewing and annotating AI-generated responses in STEM domains. My tasks included evaluating AI outputs for accuracy and quality, as well as providing feedback to enhance model training data. I adhered closely to annotation guidelines to ensure technical excellence and consistency. • Reviewed and corrected AI-generated answers in Mathematics, Physics, and Computer Science • Evaluated responses for coherence, factuality, and educational value • Provided detailed feedback to improve training data quality • Ensured all annotations met rigorous standards for technical content

I served as an AI Data Reviewer and Annotator, focusing on reviewing and annotating AI-generated responses in STEM domains. My tasks included evaluating AI outputs for accuracy and quality, as well as providing feedback to enhance model training data. I adhered closely to annotation guidelines to ensure technical excellence and consistency. • Reviewed and corrected AI-generated answers in Mathematics, Physics, and Computer Science • Evaluated responses for coherence, factuality, and educational value • Provided detailed feedback to improve training data quality • Ensured all annotations met rigorous standards for technical content

2022 - Present

Text Labeling

Scale AITextText GenerationText Summarization

I contributed to a text data labeling project with Outlier, focusing on preparing high-quality datasets for machine learning and natural language processing (NLP) applications.

I contributed to a text data labeling project with Outlier, focusing on preparing high-quality datasets for machine learning and natural language processing (NLP) applications.

2024 - 2024

Image annotation

Scale AIImageClassification

Worked on large-scale image annotation projects to support computer vision model training and evaluation. Tasks included drawing bounding boxes, polygons, and segmentation masks to accurately label objects such as vehicles, pedestrians, and products. Applied point/key point annotations for pose estimation and tracking sequences for motion analysis.

Worked on large-scale image annotation projects to support computer vision model training and evaluation. Tasks included drawing bounding boxes, polygons, and segmentation masks to accurately label objects such as vehicles, pedestrians, and products. Applied point/key point annotations for pose estimation and tracking sequences for motion analysis.

2023 - 2024

Text data Annotation and Training for AI Systems

Scale AIAudioBounding BoxEntity Ner Classification

I contributed to a large-scale data labeling project for autonomous vehicle systems, focusing on bounding boxes and segmentation for pedestrian and vehicle detection. I also worked on NER tasks for text-based AI models, ensuring precise entity classification for NLP applications. Leveraging tools like Labelbox and custom Python scripts, I optimized labeling efficiency and accuracy. The project involved over 50,000 images and 1 million text entries, where quality assurance protocols ensured 98% annotation accuracy. My expertise in Java and Python enabled seamless integration of labeled data into machine learning pipelines.

I contributed to a large-scale data labeling project for autonomous vehicle systems, focusing on bounding boxes and segmentation for pedestrian and vehicle detection. I also worked on NER tasks for text-based AI models, ensuring precise entity classification for NLP applications. Leveraging tools like Labelbox and custom Python scripts, I optimized labeling efficiency and accuracy. The project involved over 50,000 images and 1 million text entries, where quality assurance protocols ensured 98% annotation accuracy. My expertise in Java and Python enabled seamless integration of labeled data into machine learning pipelines.

2023 - 2024

Education

Work History