Nenden Amelia

Key Skills

Software

Appen

Clickworker

Data Annotation Tech

iMerit

OneForma

Scale AI

SuperAnnotate

Toloka

Telus

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Document

Top Task Types

Audio Recording

Classification

Computer Programming Coding

Text Generation

Text Summarization

Freelancer Overview

With over three years of hands-on experience in AI training and data labeling, I’ve worked on a wide range of projects across computer vision, natural language processing, and multimodal data. I’ve labeled large-scale image datasets for object detection, optimized prompts for LLMs in both English and Bahasa Indonesia, and contributed to improving model accuracy through structured QA feedback. My background in STEM (Math, Physics, Chemistry) also helps me bring analytical precision to annotation tasks. I’m skilled in using various labeling tools and platforms, collaborating with cross-functional teams, and adapting to project-specific guidelines. My ability to handle complex instructions, maintain high consistency, and meet deadlines under pressure has been proven across freelance roles with international clients. I’m passionate about AI alignment and always aim to deliver high-quality data that truly supports model performance.

Labeling Experience

Prompt Engineering & LLM Output Evaluation (English & Bahasa Indonesia)

Scale AITextText GenerationEvaluation Rating

Contributed to a fine-tuning and evaluation project involving a large language model (LLM). My responsibilities included crafting high-quality prompts across various task types (instruction-following, dialogue, summarization, etc.) and evaluating model outputs based on clarity, accuracy, helpfulness, and alignment. I worked on English and Bahasa Indonesia data, focusing on linguistic fluency, cultural appropriateness, and relevance to task instructions. This project required strict adherence to formatting guidelines, detailed documentation, and peer feedback cycles. Average daily output: 50–100 prompt-eval pairs.

Contributed to a fine-tuning and evaluation project involving a large language model (LLM). My responsibilities included crafting high-quality prompts across various task types (instruction-following, dialogue, summarization, etc.) and evaluating model outputs based on clarity, accuracy, helpfulness, and alignment. I worked on English and Bahasa Indonesia data, focusing on linguistic fluency, cultural appropriateness, and relevance to task instructions. This project required strict adherence to formatting guidelines, detailed documentation, and peer feedback cycles. Average daily output: 50–100 prompt-eval pairs.

2023

Image Annotation for Object Detection in Computer Vision AI

LabelboxImageBounding BoxClassification

This project involved labeling thousands of images for training object detection models in the computer vision domain. I was responsible for drawing accurate bounding boxes around specific target classes (e.g. pedestrians, vehicles, tools), ensuring label consistency, and adhering to strict quality guidelines. The dataset was used to train and evaluate a detection model for an international client. Tasks included multi-class classification, edge-case identification, and quality review. Annotations were double-checked through peer QA and feedback loops to maintain a 95%+ accuracy standard.

This project involved labeling thousands of images for training object detection models in the computer vision domain. I was responsible for drawing accurate bounding boxes around specific target classes (e.g. pedestrians, vehicles, tools), ensuring label consistency, and adhering to strict quality guidelines. The dataset was used to train and evaluate a detection model for an international client. Tasks included multi-class classification, edge-case identification, and quality review. Annotations were double-checked through peer QA and feedback loops to maintain a 95%+ accuracy standard.

2024 - 2024

Education

Work History