Notion
Security Engineer, Detection and Response
$185K–$280K
San Francisco, California +1 · Hybrid · Full-time · Posted Sep 28, 2026
- Location
- San Francisco, California
New York, New York - Experience
- 3+ years; relevant experience may be in detection engineering, security operations, incident response, threat hunting, or a closely related security or software engineering role.
About the job
Build and operate detection and response systems for Notion’s cloud-native environment, including production detections, platform improvements, and incident response.
What you'll do
- Build and tune detections across cloud, identity, endpoint, and SaaS environments, and improve detection lifecycle, measurement, and rollout safety.
- Develop tooling and automation for triage, investigation, enrichment, and detection authoring; translate threat intelligence and adversary TTPs into detections.
- Participate in investigations, incident response, postmortems, metrics tracking, and a shared on-call rotation.
What they ask for
- 3+ years of experience in detection engineering, security operations, incident response, threat hunting, or a closely related security or software engineering role.
- Written or tuned production detections, with attention to signal quality and reducing noise.
- Working knowledge of at least one detection or query language (Sigma, KQL, SPL, YARA-L, EQL, or Panther), or strong SQL or Python skills and willingness to learn one.
- Understanding of attacker behavior and hands-on experience with AWS, GCP, or Azure; experience using SIEM, EDR, or SOAR tools.
Nice to have
- Experience with detection-as-code workflows, large-scale SIEM/EDR/SOAR platforms, or purple-team, blue-team, or adversary-emulation exercises.
- Experience applying LLMs or agent-style tooling to security workflows, securing AI-enabled systems, or Kubernetes/container detection.
Pay
$185,000 - $280,000 per year (base salary)
Tools
Sigma, KQL, SPL, YARA-L, EQL, Panther, SQL, Python
AI focus
Build automation for security workflows, including LLM-based workflows where useful; applying LLMs or agent-style tooling is a nice-to-have.
Model-written summary · the original posting is authoritative