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Security Software Engineer II, Detection and Response
engineeringfull-timeSan Francisco, CA, US; Remote, US
SALARY
Not listed
WORK TYPE
remote
JOB TYPE
full-time
INDUSTRY
general
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About the role
What you'll do:
- 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.
What we're looking for:
- 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.
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