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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 23 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. Apply To this Job 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 23 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. Apply To this Job
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