Quality Control Academy Fellowship

$50-$150 / one-time

Fellowship
Remote
Early applicant

$50-$150

one-time

Location requirements

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Mercor is launching the Quality Control Academy, an optional professional development program for independent professionals interested in serving as quality control auditors on advanced AI evaluation and research projects.

The Academy provides structured context on how high-quality task submissions, prompts, and rubrics are evaluated in frontier AI projects. Participants gain insight into how auditors identify issues, apply grading standards, and provide actionable feedback—without prescribing how contractors must exercise their independent judgment.

Professionals who complete the Academy may choose to make themselves eligible for Mercor’s specialized quality control and auditing opportunities.


About the Academy

This is a paid, 3-hour, self-guided course designed to prepare auditors to review tasks, prompts, and rubrics against project instructions and quality standards.

The Academy focuses on evaluation frameworks, auditing mindsets, and quality control principles commonly used in AI benchmarking and research environments. It does not dictate how contractors must perform services, but instead provides shared context on how quality is assessed.

Upon completion, you may opt in to be considered for Mercor’s high-paying Quality Control Auditor projects.


What You Will Learn

Participants receive an overview of core quality control principles used across Mercor projects and the broader AI evaluation industry:

Auditor Mindset

Learn how experienced auditors approach reviews with a spec-first, objective mindset, ensuring consistency, fairness, and traceable reasoning.

Identifying Quality Issues

Understand how to spot common task failures and violations, including prompt–rubric mismatch, overspecification, accuracy errors, PII risks, and copyright concerns.

Prompt & Rubric Evaluation

Gain familiarity with how strong prompts and well-structured rubrics are reviewed, including how criteria are written to be clear, self-contained, and independently gradable.

Actionable Feedback

Learn how auditors communicate issues using structured feedback patterns that clearly explain what’s wrong, why it matters, and how to fix it, enabling efficient revisions and higher-quality outcomes.

These modules provide context and examples, not rigid rules, allowing auditors to apply their own professional judgment.


Who We Are Looking For

We welcome independent professionals from any discipline who are interested in quality control work:

  • Attention to Detail: You can carefully evaluate written work against explicit standards.

  • Analytical Rigor: You can explain why something passes or fails based on evidence.

  • Professional Judgment: You rely on structured reasoning rather than intuition or “vibes.”

  • Integrity: You evaluate work objectively and consistently.

Auditors maintain full autonomy over how they apply their expertise on any project.


Project Details & Compensation

  • Stipend: $50–$150, paid upon successful completion of the Academy and optional practice task

  • Time Commitment: ~3 hours, self-paced

  • Future Opportunities: Graduates may opt in to be considered for ongoing Quality Control Auditor roles

  • Requirements: No subscriptions required. You may reference any valid sources when applying professional judgment.


Why Participate?

Build Auditing Expertise

Learn how quality control is applied in real-world AI evaluation and benchmarking projects.

Improve Evaluation Skills

Develop a structured approach to identifying issues, enforcing standards, and giving feedback that leads to measurable improvements.

Access Specialized Opportunities

Completion makes you eligible—if you choose—to participate in Mercor’s specialized, high-rate quality control and auditing projects.

We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.



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Posted 8 months ago

$50-$150 / one-time

Fellowship · Remote