Intern

The Data Labelling Intern is responsible for preparing high-quality ground truth data from provided samples to support machine learning and AI model development. This role involves accurately reviewing, annotating, and validating data according to defined guidelines to ensure consistency, accuracy, and reliability of labeled datasets.

Key Responsibilities

• Prepare ground truth data by labeling and annotating samples (text, image, audio, video, or other data types) based on project requirements.
• Follow detailed annotation guidelines and labeling standards to ensure consistency and accuracy.
• Review and validate labeled data to identify and correct errors or inconsistencies.
• Perform quality checks and participate in audits of annotated datasets.
• Collaborate with project managers, data scientists, and quality teams to clarify requirements and resolve ambiguities.
• Maintain documentation related to labeling processes, edge cases, and decisions.
• Meet productivity and quality targets within defined timelines.

Required Skills & Qualifications

• Bachelors degree or equivalent experience in a related field (preferred but not mandatory).
• Strong attention to detail and ability to follow complex instructions accurately.
• Basic understanding of data labeling, annotation, or machine learning concepts is a plus.
• Comfortable working with annotation tools and software (training provided if needed).
• Good analytical and problem-solving skills.
• Ability to work independently and manage repetitive tasks with high accuracy.

Preferred Qualifications

• Prior experience in data annotation, data labeling, or quality analysis.
• Familiarity with AI/ML workflows and ground truth preparation.
• Experience handling large datasets.