Remote Tax Advisor (CPA/EA) for AI Training - Contractor

micro1

Northern (KY)

Hybrid

USD 83,000 - 165,000

Full time

14 days+
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Job summary

micro1 is seeking a Tax Advisor (CPA or EA) as a contractor to support tax preparation and advisory services. You’ll apply your tax expertise to train AI systems by providing high-quality, real-world input.

Work remotely in the US with a multidisciplinary team. You will annotate documents, identify edge cases in federal and state tax, and document insights to improve AI learning and model performance.

Qualifications

  • Active CPA license or Enrolled Agent credential required.
  • Proven expertise in income tax preparation and filing for individuals and/or businesses.
  • Strong ability to convey complex tax concepts clearly.
  • Experience developing rubrics, checklists, or quality standards in tax advising or education.
  • Interest in AI and technology-driven tax solutions.
  • Attention to detail and awareness of tax law nuances.
  • Prior participation in training, auditing, or peer review is a plus.

Responsibilities

  • Review and analyze real-world tax scenarios for IRS-compliant guidance.
  • Annotate tax documents to establish rubrics and best practices for AI.
  • Deliver tax prep and filing input leveraging CPA/EA experience.
  • Identify edge cases in federal/state tax to improve AI reasoning.
  • Collaborate remotely within a multidisciplinary AI-focused tax team.
  • Document insights in structured, high-quality dataset contributions.
  • Monitor AI outputs for accuracy and completeness with feedback.

Skills

CPA license
Enrolled Agent
Tax expertise
Communication

Job description

micro1 is seeking a Tax Advisor (CPA or EA) as a contractor to support tax preparation and advisory services. You’ll apply your tax expertise to train AI systems by providing high-quality, real-world input.

Work remotely in the US with a multidisciplinary team. You will annotate documents, identify edge cases in federal and state tax, and document insights to improve AI learning and model performance.

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