DevOps Engineer - AI Model Evaluator

Mercor; København; On-site; DKK 6,000 - 10,000; Full time

Job summary

Mercor partners with a leading AI research lab to support a Frontier Code Agents project. Contributors evaluate frontier AI coding models on realistic infrastructure engineering tasks, focusing on cloud platforms, Kubernetes, CI/CD, observability, and automation.

We offer sprint-based work with rapid ramp-up and compensation per accepted task. Candidates should have 2+ years in DevOps/SRE/Cloud Eng and be proficient with major cloud providers and AI coding agents.

Qualifications

• 2+ years of professional DevOps, SRE, or Cloud Engineering experience.
• Experience with cloud platforms and infrastructure automation.
• Familiarity with AI coding agent tools and reliability engineering.

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, observability, and infrastructure automation.
• Identify bugs, edge cases, reliability issues, and failure modes.
• Compare outputs from multiple frontier models and assess their strengths and weaknesses.
• Apply professional engineering judgment to realistic infrastructure engineering scenarios.

Skills

DevOps

SRE

Cloud engineering

Infrastructure as code

Observability

Tools

AWS

Azure

GCP

Kubernetes

Terraform

CI/CD

Observability tooling

Job description

About the Role

• Mercor is partnering with a leading AI research lab to support a Frontier Code Agents project.
• Contributors help evaluate and improve frontier AI coding models through structured technical assessments.
• The work focuses on realistic infrastructure engineering workflows and model evaluation.
• Spots are limited and filling quickly on a first come, first serve basis.

What You'll Do

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

Time Commitment

• Sprint based project that runs in 12-24 hour stretches based on client requirement.

Compensation

• $400 per accepted task.
• Typical tasks take approximately 2–3 hours after ramp-up.
• Compensation is tied to accepted work.

Who Should Apply

• 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 such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
• Ability to evaluate model-generated infrastructure and reliability engineering solutions.
• Experience supporting production-scale systems is preferred.