AI Model Evaluator for DevOps & Infra

Mercor

Lisboa

Presencial

EUR 394 000 - 561 000

Tempo integral

14 dias+

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Resumo da oferta

Mercor partners with a leading AI research lab to support a Frontier Code Agents project focused on infrastructure engineering and model evaluation. Contributors evaluate complex tasks, review cloud, Kubernetes, CI/CD, observability, and automation outputs, and identify bugs and edge cases using professional judgment.

The role centers on realistic infrastructure scenarios, with sprint-like, time-bound tasks and compensation per accepted task.

Qualificações

  • 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 or similar tools.
  • Ability to evaluate model-generated infrastructure and reliability engineering solutions.
  • Experience supporting production-scale systems is preferred.

Responsabilidades

  • 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.

Conhecimentos

DevOps experience
Cloud engineering
SRE practices
AI tooling familiarity

Ferramentas

AWS
Azure
GCP
Kubernetes
Terraform
CI/CD pipelines
Observability tooling

Descrição da oferta de emprego

Mercor partners with a leading AI research lab to support a Frontier Code Agents project focused on infrastructure engineering and model evaluation. Contributors evaluate complex tasks, review cloud, Kubernetes, CI/CD, observability, and automation outputs, and identify bugs and edge cases using professional judgment.

The role centers on realistic infrastructure scenarios, with sprint-like, time-bound tasks and compensation per accepted task.

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