Role overview

DevOps Engineer- AI Model Evaluator

Requirements and responsibilities

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Role Responsibilities

  • Use frontier AI coding agents to complete and evaluate complex infrastructure engineering tasks.
  • Review model-generated implementations involving cloud platforms, Kubernetes, CI/CD systems, and infrastructure automation.
  • Identify bugs, edge cases, reliability issues, and failure modes in model outputs.
  • Compare outputs from multiple frontier models to assess strengths and weaknesses.
  • Apply professional engineering judgment to realistic infrastructure engineering scenarios.

QualificationsMust-Have

  • 2+ years of professional DevOps, SRE, or Cloud Engineering experience.
  • Experience with AWS, Azure, GCP, Kubernetes, Terraform, CI/CD pipelines, or observability tooling.
  • Regular use of AI coding agents like Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
  • Ability to evaluate model-generated infrastructure and reliability engineering solutions.

Preferred

  • Experience supporting production-scale systems.

Compensation & Legal

  • $400 per accepted task.
  • Compensation is tied to accepted work.

Application Process (Takes 20–30 mins to complete)

  • Upload resume
  • AI interview based on your resume
  • Submit form

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: [email protected]
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Browse stack
FocusDevOps EngineeringRole area
Seniority signalMiddle, JuniorCandidate level
StackAWS, Azure, CI/CDPrimary skills
Location4 accepted countriesEligibility

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