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About the Role
We are seeking highly qualified Domain Experts (SMEs) to support the evaluation, testing, and improvement of advanced Large Language Models (LLMs). You will apply your subject-matter expertise to assess AI-generated responses, identify knowledge gaps, design challenging prompts, and improve model accuracy, reliability, and reasoning.
What You'll Do
Create complex, domain-specific prompts to test LLM capabilities.
Evaluate AI-generated responses for factual accuracy, reasoning, relevance, and overall quality.
Identify factual errors, logical inconsistencies, knowledge gaps, and model weaknesses.
Develop challenging test cases and edge cases that expose model limitations.
Provide clear, detailed, and evidence-based feedback to improve model performance.
You're a Good Fit If You
Required skills: Business Analysis
Master’s degree or higher in a relevant field.
At least three years of professional, research, or teaching experience.
Excellent written English communication skills.
Strong analytical and critical-thinking abilities.
Exceptional attention to detail.
Experience working with LLMs or Generative AI.
Familiarity with prompt engineering, AI evaluation, or model testing.
Experience designing challenging prompts, identifying edge cases, or stress-testing AI models.
Independent contractor engagement through Turing. Apply via the link below - Turing handles vetting, onboarding and payment.
Common Questions
Turing has a reliability rating of very reliable based on the listings we track, with an onboarding time of 5-14 days. See our full Turing review for the details behind that rating.
Applying takes you to Turing's own website, where the application is completed. We are not a recruiter or employer and do not process applications directly - we may be recorded as the referring source.
Pay varies - Independent contractor engagement through Turing. Apply via the link below - Turing handles vetting, onboarding and payment.
Create complex, domain-specific prompts to test LLM capabilities. Evaluate AI-generated responses for factual accuracy, reasoning, relevance, and overall quality. Identify factual errors, logical inconsistencies, knowledge gaps, and model weaknesses.
