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Yonas Temesgen

Yonas Temesgen

Full-stack Developer

ETHIOPIA flag
Addis Ababa, Ethiopia
$15.00/hrIntermediateAppenTelusToloka

Key Skills

Software

AppenAppen
TelusTelus
TolokaToloka
Data Annotation TechData Annotation Tech
ClickworkerClickworker
Surge AISurge AI
Micro1
MercorMercor
LabelboxLabelbox

Top Subject Matter

Software Development – Web Applications & Code Generation
Data Science & Analytics – Database Management & Data Processing
Translation & Localization – Amharic & English Linguistics

Top Data Types

TextText
ImageImage
VideoVideo

Top Task Types

Bounding Box
Entity Ner Classification
RLHF
Data Collection
Classification

Freelancer Overview

Full-stack Developer. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Education includes Bachelor of Science, Addis Ababa University (2025).

IntermediateAmharicEnglish

Labeling Experience

Image Object Detection and Annotation for Computer Vision Models

ImageBounding Box
Annotated large image datasets for computer vision model training by drawing bounding boxes around objects such as vehicles, pedestrians, traffic signs, and infrastructure elements. Ensured accurate object localization and class labeling according to strict annotation guidelines. Performed dataset quality checks, corrected annotation inconsistencies, and reviewed edge cases involving occluded or partially visible objects. Contributed to improving dataset reliability for object detection models used in real-world perception systems.

Annotated large image datasets for computer vision model training by drawing bounding boxes around objects such as vehicles, pedestrians, traffic signs, and infrastructure elements. Ensured accurate object localization and class labeling according to strict annotation guidelines. Performed dataset quality checks, corrected annotation inconsistencies, and reviewed edge cases involving occluded or partially visible objects. Contributed to improving dataset reliability for object detection models used in real-world perception systems.

2025 - 2025

Named Entity Recognition (NER) Dataset Annotation for NLP Models

TextEntity Ner Classification
Annotated large-scale text datasets to support the training of NLP models for named entity recognition tasks. Identified and labeled entities such as persons, organizations, locations, dates, and technical terms across diverse documents including articles, conversations, and technical content. Applied strict annotation guidelines to ensure consistent labeling and high dataset quality. Performed quality validation by reviewing edge cases, resolving ambiguous entity boundaries, and verifying entity relationships within context. Contributed to improving model training datasets by flagging inconsistencies and refining annotation standards to maintain high inter-annotator agreement and reliability for downstream model training and evaluation.

Annotated large-scale text datasets to support the training of NLP models for named entity recognition tasks. Identified and labeled entities such as persons, organizations, locations, dates, and technical terms across diverse documents including articles, conversations, and technical content. Applied strict annotation guidelines to ensure consistent labeling and high dataset quality. Performed quality validation by reviewing edge cases, resolving ambiguous entity boundaries, and verifying entity relationships within context. Contributed to improving model training datasets by flagging inconsistencies and refining annotation standards to maintain high inter-annotator agreement and reliability for downstream model training and evaluation.

2025 - 2025

AI Conversation Dataset Annotation for LLM Training

TextClassification
Annotated and evaluated conversational datasets used to train and improve large language models. Tasks included labeling user intents, categorizing prompts by topic and difficulty, and evaluating AI responses for correctness, coherence, and safety. Performed structured annotation across thousands of dialogue samples, identifying hallucinations, logical errors, and policy violations. Applied detailed labeling guidelines to ensure high inter-annotator agreement and consistent dataset quality. Additionally contributed to dataset refinement by flagging ambiguous prompts, suggesting improved annotation schemas, and verifying model outputs against ground truth references. Focused on maintaining high accuracy and consistency across annotations while adhering to strict quality control standards required for production AI training pipelines.

Annotated and evaluated conversational datasets used to train and improve large language models. Tasks included labeling user intents, categorizing prompts by topic and difficulty, and evaluating AI responses for correctness, coherence, and safety. Performed structured annotation across thousands of dialogue samples, identifying hallucinations, logical errors, and policy violations. Applied detailed labeling guidelines to ensure high inter-annotator agreement and consistent dataset quality. Additionally contributed to dataset refinement by flagging ambiguous prompts, suggesting improved annotation schemas, and verifying model outputs against ground truth references. Focused on maintaining high accuracy and consistency across annotations while adhering to strict quality control standards required for production AI training pipelines.

2025 - 2025

Education

A

Addis Ababa University

Bachelor of Science, Computer Engineering

Bachelor of Science
2020 - 2025

Work History

C

Cynergy Bank

Full-stack Developer

London
2025 - Present
A

Amplitude Ventures

Full Stack Developer and Mobile Team Lead

N/A
2025 - 2025