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DevOps Engineer - AI Model Evaluator - AI Trainer

Role overview

Qualifications

  • 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

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

Key facts

  • Remote from: California (USA)
  • Part time
  • Mid-level (2-5 years)
  • DevOps Engineer
  • English

Hard skills

About the company

Mercor logo

Mercor

  • Job Boards & Talent Marketplaces
  • 51–200
Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone. Today, more than 30,000 experts in our network collectively earn over $2 million a day.

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Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: DevOps / SRE / Cloud Engineer (Coding Agent Experience)
Type: Contract
Compensation: $85/hour
Location: Remote

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.

Qualifications

Must-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: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.



#hiringmercor

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