LLM Data Labeling and Annotation Specialist

About OpenTrain

OpenTrain AI is the hiring and contracting organization for this role and the #1 platform for finding and building careers in AI training and data labeling. Create a free OpenTrain account to build your profile and apply in minutes.

This contractor, part-time opportunity is open to candidates in Argentina, Chile, Finland, India, Indonesia, Nepal, the Philippines, and Poland.

• Pay: $5 USD per hour
• Work arrangement: Remote
• Engagement: Part-time contractor
• Expected commitment: 20 or more hours per week
• Working language: English

About AI Training and Data Labeling

AI training is the human work behind modern artificial intelligence. Contributors label examples, review model responses, and evaluate content so language models can learn to produce more useful and accurate results.

This project focuses on text-based English documents and LLM response data. Your careful judgments will support classification, named-entity labeling, and response evaluation in a growing field of remote, flexible technology work.

• Work directly with language-model response data
• Help improve how AI systems interpret and evaluate text
• Build experience in a fast-growing AI training field

The Role

OpenTrain AI is seeking an LLM Data Labeling and Annotation Specialist to work with approximately 500 to 1,000 rows of data. Each row contains context, a query, and an automatically generated label that must be checked through a two-step human review process.

Two agents will independently review the exact same row, followed by a third agent who performs quality assurance. At least one of the three agents must be an expert capable of assessing technical and financial content.

• Data type: Text-based English documents
• Labeling methods: Classification, entity NER classification, and evaluation rating
• Labeling software: AWS SageMaker
• Experience level: Expert

What You'll Do

You will assess context, queries, and automatically generated labels for accuracy and consistency. The workflow relies on multiple human judgments so that disagreements or questionable labels can be identified and reviewed.

Some rows may contain technical computer science material or financial documents. For content outside your expertise, you should make a best-effort label while relying on the designated expert agent to assess the specialized material.

• Review English-language LLM response rows
• Apply the appropriate classification labels
• Identify and classify named entities where required
• Rate or evaluate model responses
• Participate in duplicate review of the same rows
• Perform or support quality assurance in the three-agent process
• Use AWS SageMaker for labeling work

Requirements and Preferred Experience

Familiarity with the English language is required. Basic general knowledge is also needed to understand the context and queries being reviewed.

Experience with technical computer science data or financial documents is a plus. The project requires at least one expert among the three reviewing agents to assess technical and financial content.

• Strong familiarity with English
• Basic general knowledge
• Expert-level experience for this assignment
• Ability to commit 20 or more hours per week
• Technical computer science data experience is helpful
• Financial document experience is helpful

Why Join AI Training Work

AI training and data annotation offer a way to contribute to cutting-edge technology from anywhere with a computer or phone and an internet connection. Many projects are part-time and flexible, allowing contributors to fit work around other commitments.

In this role, your text judgments and quality checks help shape how language models understand questions and produce responses. It is a practical opportunity to apply language skills, general knowledge, and specialized expertise to real AI development.

• Remote work within the listed eligible countries
• Part-time contractor engagement
• Flexible participation within a 20-plus-hour weekly commitment
• Hands-on experience with LLM evaluation and annotation

How to Apply

Create a free profile on OpenTrain, review the opportunity details, and apply through the platform. Be prepared to highlight your English proficiency and any experience with technical computer science data or financial documents.

• Apply through OpenTrain AI
• Indicate your relevant technical or financial experience
• Confirm that you can work 20 or more hours per week
• Complete the project workflow using AWS SageMaker