AI-Driven DevOps Engineer & Model Evaluator

Mercor; San Francisco (CA); On-site; USD 15,000 - 22,000; Part time

Job summary

Mercor is partnering with a leading AI research lab to support a Frontier Code Agents project. You will help evaluate frontier AI coding models by performing infrastructure engineering tasks and reviewing model-generated implementations on cloud platforms, Kubernetes, and CI/CD tools.

This sprint-based role involves evaluating bugs, edge cases, and failure modes with professional engineering judgment, applying real-world reliability concepts to production-scale systems.

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 such as 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, 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

Observability

AI coding agents experience

Infrastructure as code

Tools

AWS

Azure

GCP

Kubernetes

Terraform

CI/CD pipelines

Observability tooling

Job description

Mercor is partnering with a leading AI research lab to support a Frontier Code Agents project. You will help evaluate frontier AI coding models by performing infrastructure engineering tasks and reviewing model-generated implementations on cloud platforms, Kubernetes, and CI/CD tools.

This sprint-based role involves evaluating bugs, edge cases, and failure modes with professional engineering judgment, applying real-world reliability concepts to production-scale systems.