Muhammad Andhika Oktaroza

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Top Task Types

Translation/Localization

Freelancer Overview

Indonesian Language and Culture Expert with over four years of experience in data annotation, content validation, and quality assurance for large-scale digital platforms. Skilled in ensuring accuracy, consistency, and cultural relevance of datasets to support AI model training and natural language processing. Adept at handling high-volume annotation tasks with precision, applying industry standards to deliver structured and reliable outputs. Proficient in using annotation tools (Labelbox, Label Studio) and productivity platforms to manage multilingual projects efficiently. Experienced in collaborating with cross-functional teams to refine AI workflows and provide linguistic insights that enhance model performance. Strong foundation in Bahasa Indonesia linguistics, cultural adaptation, and data-driven decision-making, contributing to AI teams focused on language understanding and localization.

Labeling Experience

Data Annotator

Scale AITextPrompt Response Writing SFT

Contributed to the development of AI language models by performing text classification, labeling, and quality checks on Indonesian and English datasets. Tasks included prompt–response evaluation (SFT), factual accuracy checks, red teaming for safety violations, and text generation assessments. Ensured outputs met accuracy, consistency, and cultural relevance standards, while applying annotation guidelines to maintain high-quality datasets for supervised fine-tuning and reinforcement learning with human feedback (RLHF). Collaborated with project managers through online platforms, consistently meeting daily annotation targets with 95%+ accuracy.

Contributed to the development of AI language models by performing text classification, labeling, and quality checks on Indonesian and English datasets. Tasks included prompt–response evaluation (SFT), factual accuracy checks, red teaming for safety violations, and text generation assessments. Ensured outputs met accuracy, consistency, and cultural relevance standards, while applying annotation guidelines to maintain high-quality datasets for supervised fine-tuning and reinforcement learning with human feedback (RLHF). Collaborated with project managers through online platforms, consistently meeting daily annotation targets with 95%+ accuracy.

2025

Education

Work History