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Security Engineer, Detection & Response

Anthropic· Security
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Hybrid (San Francisco, CA), Hybrid (New York City, NY), Hybrid (Seattle, WA), Hybrid (Washington, DC)$300k - $405kPosted 11mo ago

About the role

Security Engineer focused on detection, incident response, and tooling development to monitor for threats, investigate incidents, and coordinate responses across Anthropic’s technology stack.

Responsibilities

  • Lead cybersecurity Incident Response efforts covering diverse domains from external attacks to insider threats involving all layers of Anthropic’s technology stack
  • Develop and deploy novel tooling that may leverage Large Language Models to enhance detection, investigation, and response capabilities
  • Create and optimize detections, playbooks, and workflows to quickly identify and respond to potential incidents
  • Review Incident Response metrics and procedures and drive continuous improvement
  • Work cross functionally with other security and engineering teams
  • Note: This position will require participation in an on-call rotation

Qualifications

  • 3+ years of software engineering experience, with security experience a plus and/or
  • 5+ years of detection engineering, incident response, or threat hunting experience
  • A solid understanding of cloud environments and operations
  • Experience working with engineering teams in a SaaS environment
  • Exceptional communication and collaboration skills
  • An ability to lead projects with little guidance
  • The ability to pick up new languages and technologies quickly
  • Experience handling security incidents and investigating anomalies as part of a team
  • Knowledge of EDR, SIEM, SOAR, or related security tools

Skills

Required:Software engineeringDetection engineeringIncident responseCloud environmentsCommunication and collaboration
Last updated Jul 8, 2026. Interested? Apply on the company site.

About Anthropic

building reliable, interpretable, and steerable AI systems

Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.

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