R Model Evaluation Engineer

As an R Model Evaluation Engineer, you will review AI-generated answers and judge whether they are accurate, clear, well reasoned, and responsive to the prompt. You will use R and applied statistics to identify mistakes and create better examples for AI training.

• Review AI-generated responses for accuracy, clarity, prompt adherence, and step-by-step reasoning.
• Find errors in statistical methods, modeling choices, and data-wrangling workflows.
• Fact-check analytical results and compare multiple model responses for correctness.
• Write expert explanations and model solutions that show correct use of R.
• Create detailed prompts and responses for AI training across different topics.
• Test model outputs for inaccuracies or bias and assess their reliability across use cases.

This is remote, part-time contract work for an intermediate-level contributor with strong R, statistics, and data-analysis experience. You must be able to write clearly in English and meet the location and time requirements below.

• Pay: $55 per hour in USD.
• Time: 20+ hours per week.
• Work arrangement:
  Remote contract work.
• Eligible locations:
  Bangladesh, Bhutan, Brazil, Cambodia, Germany, India, Indonesia, Malaysia, Nepal, Pakistan, Singapore, Sri Lanka, Thailand, the Philippines, the United States, Timor-Leste, and Vietnam.
• Language:
  English.
• Experience:
  
  At least 2 years of hands-on R experience in data analysis, statistics, or data science.
• R skills:
  Strong R programming, including data wrangling, functional programming patterns, and reusable functions or packages.
• Statistics:
  Applied knowledge of regression, statistical inference, and model validation.
• Analysis experience:
  Ability to build end-to-end R analyses covering data cleaning, exploratory analysis, modeling, and visualization.
• Tools and systems:
  Familiarity with tidyverse, data.table, ggplot2, and large language models used for coding or code review.
• Education and communication:
  Excellent English writing skills and a bachelor's degree in Statistics, Mathematics, Computer Science, or a closely related quantitative field.

AI training work uses human review, examples, and feedback to help artificial intelligence produce better results. People with strong technical experience are paid to check model behavior, explain what is wrong, and create reliable examples that improve future outputs.