Remote Econometrics Instructor for AI Training

Alignerr Corp.; Sydney; On-site; AUD 83,000 - 124,000; Part time

Job summary

Alignerr Corp. is seeking experienced econometrics instructors and quantitative economics professionals to help evaluate and improve AI training data and model explanations.

This remote, hourly contract offers flexible scheduling for experts who want to shape how AI reason about statistics and causal inference. You will review questions, provide feedback, and apply real-world teaching experience to ensure content is accurate and pedagogically sound.

Qualifications

• 3+ years of experience teaching econometrics or quantitative economics.
• Strong command of regression analysis, causal inference, and core statistical methods.
• Able to clearly evaluate and articulate quantitative reasoning at an expert level.
• Comfortable reviewing structured, technical content with precision and consistency.
• Self-motivated and reliable in an independent, remote work environment.

Responsibilities

• Review and validate econometric questions, models, and explanations used in AI training datasets
• Assess statistical accuracy, methodological assumptions, and interpretation of results
• Identify errors, edge cases, and reasoning gaps in AI-generated quantitative content
• Provide clear, structured feedback to improve model reasoning and explanation quality
• Apply your real-world teaching experience to ensure content is both accurate and pedagogically sound
• Work independently and asynchronously to complete task-based assignments

Skills

Econometrics teaching

Regression analysis

Causal inference

Quantitative economics

Independent remote work

Education

Master's or PhD in Economics, Statistics, or related field

Tools

R

Stata

Python

Job description

Alignerr Corp. is seeking experienced econometrics instructors and quantitative economics professionals to help evaluate and improve AI training data and model explanations.

This remote, hourly contract offers flexible scheduling for experts who want to shape how AI reason about statistics and causal inference. You will review questions, provide feedback, and apply real-world teaching experience to ensure content is accurate and pedagogically sound.