AI Content Expert II Co-op

Qualifications

• Currently enrolled in Bachelor's degree in a relevant field
• Graduation date of May 2026 or later
• Experience working with written language data, including experience with annotation, and other forms of data markup
• Strong proficiency in English with excellent writing, reading, and comprehension skills (C2 level in the Common European Framework CEFR scale)
• Strong research skills to gather relevant information, understand complex topics, and synthesize multiple resources
• Excellent attention to details and ability to focus for a long period of time
• Comfortable with high-school level STEM
• Ability to effectively write and evaluate diverse subject matter across various domains
• Ability to adapt writing style to suit various style guidelines and customers

Responsibilities

• Create complex training data in multiple modalities (text, image, video) on various topics, including technical or science-related content
• Write grammatically correct texts in different styles with various degrees of creativity, strictly adhering to provided guidelines
• Audit and perform quality checks of tasks completed by other specialists, if required
• Making sound judgments and logical decisions when faced with ambiguous or incomplete information while performing tasks
• Dive deep into issues and implement solutions independently
• Identify and report tooling bugs and suggest improvements
• As part of the Data Team, you will work closely with scientists and engineers to review and update guidelines, identify tooling improvement opportunities, and engage in conversations regarding the quality of data

Full Description

Job Summary

The AI Content Expert II Co-op role at Amazon involves creating high-quality training data to improve and expand AGI's Large Language Models' (LLMs) capabilities.

Key Responsibilities:

• Create complex training data in multiple modalities (text, image, video) on various topics, including technical or science-related content.

• Write grammatically correct texts in different styles withvarious degrees of creativity, strictly adhering to provided guidelines.

• Audit and perform quality checks of tasks completed by other specialists, if required.

• Making sound judgments and logical decisions when faced with ambiguous or incomplete information while performing tasks.

• Dive deep into issues and implement solutions independently.

• Identify and report tooling bugs and suggest improvements.

A Day in the Life:

This is a 6-month full-time (40 hours per week) Co-op role from July - December 2025. As part of the Data Team, you will work closely with scientists and engineers to review and update guidelines, identify tooling improvement opportunities, and engage in conversations regarding the quality of data.

Basic Qualifications:

• Currently enrolled in Bachelor's degree in a relevant field.

• Graduation date of May 2026 or later.

• Experience working with written language data, including experience with annotation, and other forms of data markup.

• Strong proficiency in English with excellent writing, reading, and comprehension skills (C2 level in the Common European Framework CEFR scale).

• Strong research skills to gather relevant information, understand complex topics, and synthesize multiple resources.

• Excellent attention to details and ability to focus for a long period of time.

• Comfortable with high-school level STEM.

• Ability to effectively write and evaluate diverse subject matter across various domains.

• Ability to adapt writing style to suit various style guidelines and customers.

Preferred Qualifications:

• Experience with creating complex data for LLM training and evaluation.

• 1+ year(s) of experience working with command line interfaces and basic UNIX commands.

• Familiarity with common markup languages such as HTML, XML, Markdown.

• Familiarity with common standard text formats such as JSON, CSV, RTF.

• Working knowledge of Python or another scripting language.

• Familiarity with regular expressions syntax.

• Familiarity with Large Language Models.

• Comfort in annotation work that may include sensitive content.