About this role
Role Overview
Evaluate and improve frontier AI coding models by using AI coding agents to complete and assess realistic infrastructure engineering workflows. Work will focus on reviewing model-generated infrastructure solutions across cloud platforms, container orchestration, CI/CD, observability, and infrastructure automation, and on identifying reliability issues and failure modes.
Key Responsibilities- Use frontier AI coding agents to complete and evaluate complex infrastructure engineering tasks.
- Review and assess model-generated implementations for cloud platforms, Kubernetes, CI/CD pipelines, observability tooling, and infrastructure as code.
- Identify bugs, edge cases, reliability problems, and failure modes in model outputs.
- Compare outputs from multiple frontier models, documenting strengths and weaknesses of each.
- Apply professional engineering judgment to realistic infrastructure and reliability scenarios.
- At least 2 years of professional experience in DevOps, SRE, or Cloud Engineering.
- Practical experience with one or more cloud providers: AWS, Azure, or GCP.
- Hands-on experience 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.
- Demonstrated ability to evaluate model-generated infrastructure and reliability engineering solutions.
- Experience supporting production-scale systems is preferred.
- Location: Remote.
- Employment type: hourly.
- Project work is sprint based, running in 12 to 24 hour stretches based on client requirements.
- Typical tasks require approximately 2 to 3 hours of work after initial ramp-up.
- Spots are limited and filled on a first come, first serve basis.
- Listed hourly rate: $85 per hour.
- Alternate compensation item: $400 paid for each accepted task.
- Compensation is tied to accepted work.
- Open to remote contributors who meet the qualifications above.
- Placement depends on available project spots, which are limited and filled on a first come, first serve basis.