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SWE-Bench Task Auditor

$90
per hour
Contractor Remote Last verified 21 Jul 2026
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About the Role

Evaluate the quality, correctness, and reproducibility of software-engineering benchmark tasks used to train and evaluate a frontier AI lab's models. You'll assess repository-level tasks, reference patches, test harnesses, and grading integrity — and provide clear, rubric-based written feedback.

What You'll Do

    You're a Good Fit If You

    • 3+ years professional software engineering
    • Real open-source contribution or maintainer experience (merged PRs, committer / maintainer roles)
    • Strong ability to audit reference patches, test runners, and Docker isolation, and to detect answer leakage / reward hacking
    • Fluency across common ecosystems (Python and at least one of Java / Go / TypeScript / C++)
    • Familiarity with SWE-Bench (Verified) or similar repository benchmarks
    • Maintainer history on major Python OSS (Django, Flask, scikit-learn, sympy, pytest, etc.)
    • Prior code-review or task-grading experience

    Role Highlights

    Pay
    $70-$90/h
    Contract
    Contractor
    Location
    Remote
    Category
    Model evaluation
    Posted
    2 months ago
    Job ID
    WE-7842

    Pay & Payout

    Independent contractor engagement. Apply via the link below - Mercor handles onboarding and pays weekly via Stripe or Wise.

    Common Questions

    Mercor has a reliability rating of very reliable based on the listings we track, with an onboarding time of 1-3 weeks (interview + assessment + trial). See our full Mercor review for the details behind that rating.

    Applying takes you to Mercor's own website, where the application is completed. We are not a recruiter or employer and do not process applications directly - we may be recorded as the referring source.

    $70-$90/h - Independent contractor engagement. Apply via the link below - Mercor handles onboarding and pays weekly via Stripe or Wise.

    Evaluate the quality, correctness, and reproducibility of software-engineering benchmark tasks used to train and evaluate a frontier AI lab's models. You'll assess repository-level tasks, reference patches, test harnesses, and grading integrity — and provide clear, rubric-based written feedback.

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