Finance Lecturer (AI Training)

Alignerr

🏠 Remote
📍Boston, MA
Posted May 26, 2026
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Job Overview

Position

Finance Lecturer (AI Training)

Company

Alignerr

Location

Boston, MA

Work Type

Remote

Job ID

li-4419125234

Job Description

About The Role
We're looking for finance lecturers and economists to help shape how the next generation of AI understands financial and economic reasoning. Your subject-matter expertise will directly influence the accuracy and quality of AI systems used by millions — making this one of the most impactful ways to apply your knowledge outside the classroom.

  • Organization: Alignerr (Powered by Labelbox)

  • Type: Hourly Contract

  • Location: Remote

  • Commitment: 10–40 hours/week

What You'll Do

  • Review and evaluate finance-related questions, explanations, and reasoning used in AI training

  • Assess the accuracy of core financial concepts including valuation, risk, capital markets, and macroeconomics

  • Identify errors, misconceptions, and gaps in AI-generated financial content

  • Provide clear, structured feedback to improve the quality and depth of financial reasoning

  • Apply your teaching experience to catch the kinds of misunderstandings that trip up students — and AI alike

  • Work independently and asynchronously at a pace that suits your schedule

Who You Are

  • 3+ years of experience teaching finance or economics at the postsecondary level

  • Strong command of corporate finance, financial markets, or financial economics

  • Able to critically evaluate written financial reasoning and communicate feedback clearly

  • Comfortable reviewing structured content systematically and at scale

  • Self-motivated and reliable when working independently

Nice to Have

  • Graduate degree in Finance, Economics, or a related field

  • Industry or applied finance experience (investment banking, asset management, consulting, etc.)

  • Familiarity with AI systems, model evaluation, or data annotation workflows

Why Join Us

  • Work on cutting-edge AI projects with top research labs

  • Fully remote and flexible — work on your own schedule, from anywhere

  • Freelance perks: autonomy, variety, and global collaboration

  • Use your expertise in a high-impact, intellectually stimulating way

  • Potential for ongoing work and contract extension
✦

Interview Prep

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Key Skills

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Preferred:

Practice Questions

💡Technical Questions (3)
  • 1.If an AI model generates an explanation stating that a company's Weighted Average Cost of Capital (WACC) decreases indefinitely as it takes on more debt because debt is cheaper than equity, how would you evaluate and correct this reasoning?
  • 2.How would you systematically evaluate an AI's explanation of the Capital Asset Pricing Model (CAPM) to ensure it is both technically accurate and pedagogically sound for training purposes?
  • 3.Suppose you are reviewing AI-generated content on macroeconomic policy, and the model asserts that expansionary monetary policy always leads to immediate GDP growth. How would you address this?
🎯Behavioral Questions (3)
  • 1.Tell me about a time you identified a persistent misconception among your students that was difficult to correct. How did you handle it?
  • 2.Describe a situation where you had to review a large volume of academic or financial content systematically. How did you ensure accuracy and avoid fatigue?
  • 3.Give an example of a time you had to work independently on a complex project with minimal supervision. How did you stay motivated and on track?
🧩Situational Questions (2)
  • 1.You encounter an AI-generated finance explanation that is factually accurate but uses overly complex jargon that would confuse an introductory student. How do you provide feedback?
  • 2.While reviewing content, you come across a valuation methodology that you personally disagree with, but it is an accepted practice in certain sectors. The AI presents it as the only valid approach. How do you evaluate this?

Resume Keywords

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Finance LecturerAI TrainingCurriculum EvaluationCorporate FinanceMacroeconomicsFinancial ReasoningError IdentificationPedagogical FeedbackData AnnotationAsynchronous Work

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