Yonas T.

Key Skills

Software

Appen

Telus

Toloka

Data Annotation Tech

Clickworker

Surge AI

Micro1

Mercor

Labelbox

Top Subject Matter

Software Development – Web Applications & Code Generation

Data Science & Analytics – Database Management & Data Processing

Translation & Localization – Amharic & English Linguistics

Top Data Types

Text

Image

Video

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).

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.

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.

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.

2025 - 2025

Education

Work History