Senior/staff Security Engineer, Threat Intelligence

Anthropic Anthropic · AI Frontier · Zürich, Switzerland · Security

This role focuses on cyber threat intelligence, building tooling and pipelines to operationalize indicators into defenses, conducting threat hunts, and performing technical analysis of malware and attacker tooling. It requires strong Python skills and experience with threat actors and their TTPs, primarily for protecting AI labs and cloud infrastructure.

What you'd actually do

  1. Research, track, and report on threat actors and campaigns targeting AI labs, cloud infrastructure, and the broader technology sector — producing timely, actionable intelligence for Security Engineering stakeholders
  2. Build and maintain tooling and automated pipelines to collect, enrich, correlate, and operationalize indicators of compromise into our detection and alerting stack
  3. Develop and execute intelligence-driven threat hunts across endpoint, cloud, identity, and SaaS telemetry, and turn findings into durable detections
  4. Perform technical analysis of malware, phishing infrastructure, and attacker tooling to extract indicators, TTPs, and attribution signals
  5. Partner with Detection Engineering and Incident Response to translate intelligence into detection rules, hunting hypotheses, and incident context in near-real-time

Skills

Required

  • cyber threat intelligence
  • threat hunting
  • intrusion analysis
  • nation-state or advanced criminal threat actors
  • tooling, infrastructure patterns, tradecraft, and targeting
  • production-quality Python
  • automation and data pipelines
  • malware analysis
  • infrastructure analysis (passive DNS, certificate pivoting, netflow)
  • log analysis
  • detection logic (YARA, Sigma, Snort/Suricata, or SIEM-native queries)
  • clear and concise writing

Nice to have

  • existing network in the threat intelligence community
  • experience sharing intelligence productively
  • defending cloud-native and research-heavy environments (AWS/GCP, Kubernetes, ML infrastructure, developer tooling and supply chain)
  • applying LLMs or other AI tooling to accelerate intelligence collection, enrichment, and analysis
  • public research, conference talks, or open-source tooling contributions in the CTI space

What the JD emphasized

  • sophisticated adversaries
  • nation-state
  • advanced criminal actors
  • production-quality Python
  • build the tooling you need end-to-end
  • malware analysis
  • infrastructure analysis
  • log analysis
  • authoring detection logic