General Data Annotation Specialist

Role Overview

You will be part of a team that prepares raw information for machine learning systems, turning unstructured or messy data into clean, labeled datasets that models can learn from. Day to day, you’ll work inside annotation tools and spreadsheets, reading through guidelines, then applying those rules consistently to each item you process. The quality of your work has a direct effect on how accurately AI systems recognize patterns, interpret language, and respond in real-world situations, so your precision matters beyond just hitting a daily quota.

Key Responsibilities

• Read and interpret project-specific annotation guidelines before beginning each batch of work, making sure you understand the exact expectations for every data type you handle.
• Review text, images, audio clips, or documents and assign the correct labels, categories, or classifications according to the instructions provided.
• Extract specific pieces of information from records or files and enter them into structured formats without losing accuracy or skipping relevant details.
• Flag items that are ambiguous, broken, or poorly defined so senior team members can review them and adjust the guidelines if needed.
• Work steadily toward daily accuracy and productivity targets, balancing speed with careful judgment so that quality never falls below the required standard.
• Act on feedback from quality checks or project leads, adjusting your approach to correct recurring mistakes and reduce the need for rework.
• Maintain a consistent interpretation of rules across all assignments, even when similar-looking data seems to vary in subtle ways.
• Report workflow issues, tool problems, or unclear instructions promptly so the team can resolve them before they affect output.

Requirements & Qualifications

• Strong command of written English, including the ability to read instructions carefully and understand subtle differences in meaning or context.
• Demonstrated attention to detail, with a track record of spotting small errors or inconsistencies that others might overlook.
• Comfort working on repetitive, screen-based tasks for extended periods while maintaining focus and composure.
• Ability to follow structured guidelines precisely without improvising or introducing your own interpretation where rules are already defined.
• Solid problem-solving skills, especially when deciding how to handle edge cases or incomplete data that does not fit neatly into a category.
• Basic digital literacy, including experience with spreadsheets, web-based tools, and cloud document systems.
• Accurate typing and data entry skills, with a reasonable words-per-minute speed and low error rate.
• Previous experience in data annotation, data entry, quality assurance, or a similar detail-heavy role is a plus, but not a hard requirement.