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# DevOps Engineer for AI Model Evaluation

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $176,800.0
- **Contract:** Permanent contract
- **Skills:** Microsoft Windows, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Engineering, Continuous Integration, Cursor (Graphical User Interface Elements), Linux, DevOps, Reliability Engineering, Datadog, Kubernetes, Infrastructure Automation Frameworks, Terraform
- **Published:** August 4, 2026
- **Apply:** https://www.careerjet.com/job/register/us1f36c5566d1f4c982fe9dea84e326ade

## About the Role

* Minimum 2 years of professional experience in DevOps, SRE, or Cloud Engineering.
 * Hands-on experience with one or more cloud platforms, such as AWS, Azure, or GCP.
 * Familiarity with Kubernetes, Terraform, CI/CD pipelines, and 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., Are you experienced in managing Windows and Linux systems with a passion for building highly-available infrastructure at massive scale? Join us in driving cloud innovation and auto…
 + 11 days ago

## Description

This role supports a Frontier Code Agents initiative at a leading AI research lab, focused on evaluating and improving advanced AI coding models by performing structured technical assessments of realistic infrastructure engineering workflows and model outputs. Key Responsibilities

 * Use frontier AI coding agents to perform and evaluate complex infrastructure engineering tasks.
 * Review model-generated implementations that involve cloud platforms, Kubernetes, CI/CD systems, observability, and infrastructure automation.
 * Identify bugs, edge cases, reliability problems, and likely failure modes in model outputs.
 * Compare outputs from multiple frontier models, assessing relative strengths and weaknesses.
 * Apply professional engineering judgment to realistic infrastructure and reliability engineering scenarios.


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