Security Software Engineer II, Detection and Response

Pinterest is seeking an experienced Security Engineer to build and implement detection and response improvements and adapt to emerging threats to protect employees and infrastructure. In this role, you will have the opportunity to solve challenging problems and provide a meaningful impact on our overall security posture.

Responsibilities:

• Build alerts and automation workflows to improve capabilities to detect and response to external and internal security threats
• Manage our logging pipelines and infrastructure and onboard new logging sources to improve our detection coverage
• Develop and maintain internal tooling to expand and automate team detection and response capabilities
• Respond to alerts generated from our tooling and run incidents as part of an on-call rotation
• Collaborate with cross team partners
• Hunt for previously undetected threats in our environment
• Leverage AI to streamline and enhance the efficiency, accuracy, and coverage of security engineering

Requirements:

• Bachelor's degree in Computer Science, Cybersecurity or, a related field or equivalent experience
• Strong knowledge of intrusion detection and incident response with an engineering focus in a modern cloud first environment
• Knowledge of the attacker lifecycle, common attack and detection techniques
• Hands on experience with writing SIEM queries for alerting, response, and threat hunting
• Experience consuming threat intel and applying it to improve detection capabilities
• Familiarity with using multiple sources of telemetry for threat investigations: Eg. EDR, Osquery, Firewall logs
• Understanding of networking technologies and/or network security, basic TCP/IP network fundamentals
• Depth in ideally MacOS internals, or alternatively in Linux/UNIX or Windows internals, persistence mechanisms, privilege escalation techniques
• Scripting or automation experience (e.g., Python, Go, Ruby) for tool development or integration
• Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs
• Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)
• High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables