Structured Data LLM Evaluation Annotator

About OpenTrain

OpenTrain AI is hiring contractors for meaningful work in AI training and data labeling. OpenTrain helps people start and grow careers teaching AI by connecting them with projects, supporting professional profiles, and making it easier to build a lasting portfolio of relevant experience.

Creating an OpenTrain account is free, and candidates can apply in minutes for opportunities that match their skills and availability.

• Remote contractor work
• Flexible part-time commitment options
• A chance to build experience in a fast-growing AI field

About AI Training and LLM Evaluation

Modern AI systems learn and improve through carefully prepared examples and human feedback. LLM evaluation is part of this process: skilled reviewers test model outputs, identify weaknesses, and explain which responses are accurate, logical, complete, and supported by evidence.

In this role, your analysis will help reveal hallucinations, reasoning gaps, and edge cases in generative AI systems.

• Work directly with AI-generated text and structured datasets
• Use evidence-based judgments to support model improvement
• Contribute to the development of increasingly capable AI systems

The Role

OpenTrain AI is seeking a Structured Data LLM Evaluation Annotator to create challenging prompts and assess how large language models retrieve, analyze, and reason over structured information. You will review responses against provided datasets and supporting evidence, then document your conclusions clearly and consistently.

This is a remote contractor assignment lasting four weeks. Available commitment levels are 20, 30, or 40 hours per week, with at least four hours of overlap with Pacific Time. Compensation is described as competitive; no specific rate or payment unit is provided.

• Assignment length: four weeks
• Commitment options: 20, 30, or 40 hours per week
• Required schedule overlap: at least four hours with Pacific Time
• Work arrangement: remote contractor assignment

What You'll Do

You will design evaluation tasks and make careful judgments about the quality of AI-generated responses. The work requires close attention to source data, supporting evidence, and the reasoning used to reach an answer.

Your written findings should be concise, specific, and grounded in the available evidence so that model weaknesses can be understood and addressed.

• Create challenging prompts that test retrieval, analysis, and reasoning over structured data
• Evaluate responses for factual accuracy, logical reasoning, completeness, and consistency
• Identify hallucinations, reasoning gaps, model failures, and edge cases
• Validate model outputs against provided datasets and supporting evidence
• Document findings with concise, evidence-based explanations
• Apply annotation guidelines consistently and maintain high-quality standards

Requirements

Applicants must have a master's degree or higher in any discipline and at least three years of professional, research, or teaching experience. The role calls for strong analytical and critical-thinking abilities, excellent written English, and the ability to explain clearly why a model response is correct or incorrect.

You should be comfortable evaluating claims against structured data and supporting evidence while maintaining consistent annotation quality.

• Master's degree or higher in any discipline
• At least three years of professional, research, or teaching experience
• Strong analytical, critical-thinking, and information-validation skills
• Excellent written English communication skills
• Ability to evaluate LLM responses against structured data and supporting evidence
• Careful attention to detail and consistent application of guidelines

Helpful Background

Experience with large language models, generative AI, prompt engineering, AI evaluation, data annotation, or model testing is valuable. Familiarity with structured datasets can also help you work effectively with the materials used in evaluation tasks.

• Prompt engineering or prompt design
• Large language models and generative AI
• AI evaluation or model testing
• Data annotation
• CSV files, Excel workbooks, or databases
• Designing prompts that reveal model limitations

Apply Through OpenTrain

AI training and data-labeling work is a flexible way to participate in the technology shaping modern artificial intelligence. Many projects are remote and can fit around other professional, academic, or personal commitments.

Create a free OpenTrain account to build your AI training profile and apply for this Structured Data LLM Evaluation Annotator assignment.

• Apply remotely through OpenTrain
• Select a supported weekly commitment of 20, 30, or 40 hours
• Bring analytical judgment and clear written explanations to cutting-edge AI work