Sief El-rashedei

Key Skills

Software

Clickworker

Data Annotation Tech

Labelbox

Remotasks

Scale AI

Toloka

V7 Labs

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Audio Recording

Data Collection

Prompt Response Writing SFT

Question Answering

Translation Localization

Freelancer Overview

I have over a year of hands-on experience in AI training data and data labeling, with a strong focus on computer vision tasks. My work has primarily involved recording and localizing visual data for autonomous systems, contributing to high-quality datasets used in model development and evaluation. I’ve worked extensively on supervised fine-tuning (SFT) pipelines, ensuring that AI models are trained on accurate, diverse, and contextually relevant data. In addition to data labeling, I specialize in vision-language prompt engineering and dataset internationalization (i18n), helping adapt AI systems to multiple languages and cultural contexts. My attention to detail, efficiency in handling large-scale datasets, and ability to collaborate in cross-functional AI teams set me apart in delivering data solutions that directly impact model performance and global deployment readiness

Labeling Experience

Vision Prompt & Localization Labeling for Autonomous Driving Data

Scale AIImageBounding BoxClassification

Worked on a large-scale data labeling project for autonomous driving systems. Tasks included annotating images with bounding boxes and segmentation masks to identify road elements (vehicles, traffic signs, pedestrians, lane markings, etc.), as well as classifying scene context. Additionally, contributed to vision-language prompt writing and multilingual localization (i18n) to support fine-tuning and evaluation of AI models. Quality was maintained through multi-step review cycles and internal QA standards, ensuring high annotation accuracy.

Worked on a large-scale data labeling project for autonomous driving systems. Tasks included annotating images with bounding boxes and segmentation masks to identify road elements (vehicles, traffic signs, pedestrians, lane markings, etc.), as well as classifying scene context. Additionally, contributed to vision-language prompt writing and multilingual localization (i18n) to support fine-tuning and evaluation of AI models. Quality was maintained through multi-step review cycles and internal QA standards, ensuring high annotation accuracy.

2023 - 2024

Education

Work History