DevOps Engineer - AI Model Evaluator

Mercor; Amsterdam; On-site; EUR 167,000 - 215,000; Part time

Job summary

Mercor in Amsterdam collaborates with a leading AI research lab on Frontier Code Agents, evaluating and improving frontier AI coding models through structured technical assessments. You review cloud platforms, Kubernetes, CI/CD, observability, and infrastructure automation; identify bugs and edge cases; and compare outputs from multiple frontier models to assess strengths and weaknesses.

This sprint-based engagement runs 12–24 hour stretches per client requirements, with compensation tied to

Qualifications

• 2+ years of professional DevOps, SRE, or Cloud Engineering experience.

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

AWS

Azure

GCP

Kubernetes

Terraform

CI/CD

Observability

Tools

AWS

Azure

GCP

Kubernetes

Terraform

CI/CD pipelines

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.