Blend threat intelligence with ML/automation to cut Cloudflare's detect-and-respond time

A hybrid Threat Intelligence + ML/automation engineering role at Cloudflare, building detection use cases, SOAR playbooks, and IOC/IOA pipelines to reduce MTTD/MTTR. Interview will test threat actor/TTP knowledge (MITRE ATT&CK), applied ML for detection, Python-based security automation, and communicating risk to both technical and executive audiences.

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What this interview tests

Threat actor/TTP profiling using MITRE ATT&CK
Machine learning for detection use cases (full lifecycle)
SOAR playbook design & Python automation via APIs
IOC/IOA ingestion, enrichment, and correlation
Communicating risk to technical vs. executive audiences

Common question themes

Walk through a threat actor or TTP profile you built and how it became an actual detection

Describe a machine learning detection use case you took from data to production

Tell me about a SOAR playbook or automation you built and what manual process it replaced

How do you handle IOC/IOA enrichment and correlation across multiple threat feeds

How would you explain the same emerging threat to a security engineer versus an executive

How candidates describe it

Real Senior Threat Intelligence Engineer interview stories — retold from candidates' public write-ups, with sources.

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