AI Content Expert II, AGI Data Services

AI Content Expert II

Amazon is looking for an AI Content Expert II to help with annotations, content generation, and data analysis. As part of the Data Team, you will be responsible for delivering high-quality training data to improve and expand AGI's large language models' capabilities. Key job responsibilities include creating and annotating high-quality complex training data in multiple modalities (text, image, video) on various topics, including technical or science-related content. You will also be writing grammatically correct texts in different styles with various degrees of creativity, strictly adhering to provided guidelines. Additionally, you will be performing audits and quality checks of tasks completed by other specialists, if required. You will make sound judgments and logical decisions when faced with ambiguous or incomplete information while performing tasks. Diving deep into issues and implementing solutions independently, identifying and reporting tooling bugs and suggesting improvements.

Basic Qualifications:

• An associate's degree or related work experience.
• 2+ years of experience working with written language data, including experience with annotation, and other forms of data markup.
• Strong proficiency in English. Candidate must demonstrate excellent writing, reading, and comprehension skills (C2 level in the Common European Framework CEFR scale).
• Strong understanding of U.S.-based culture, society, and norms.
• Strong research skills to gather relevant information, understand complex topics, and synthesize multiple resources; understanding of basic academic integrity, i.e. plagiarism.
• 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.
• Ability to adapt well to fast-paced environments with changing circumstances, direction, and strategy.

Preferred Qualifications:

• Bachelor's degree in a relevant field or equivalent professional experience.
• 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.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations. If the country/region you're applying in isn't listed, please contact your Recruiting Partner. Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $40,400/year in our lowest geographic market up to $86,500/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.