Security Engineer, Detection and Response

OpenAI OpenAI · AI Frontier · Sydney, Australia · Security

Security Engineer focused on building and operating systems for detecting suspicious activity and responding to security incidents across OpenAI's infrastructure, products, and research environments. The role involves engineering detection pipelines, automating response workflows, partnering with other teams, and evaluating emergent security concerns in a frontier AI lab.

What you'd actually do

  1. Build and evolve Detection & Response capabilities across OpenAI’s infrastructure, products, and research environments, with an emphasis on high-signal detection and reliable operational response.
  2. Engineer detection pipelines and tooling: develop rule lifecycle management, measurement/quality loops (coverage, precision, latency), tuning processes, and safe rollout patterns.
  3. Automate response and investigations by building workflows that reduce toil (triage, enrichment, containment, evidence capture) and improve time-to-understand/time-to-contain.
  4. Partner with other Security teams and system/infrastructure owners across the company to ensure new systems ship with the right telemetry, threat models, and response playbooks from day one.
  5. Define D&R requirements and drive visibility across endpoints, identity, SaaS, cloud, Kubernetes: identify telemetry/control gaps, prioritize them, and advocate for fixes with partner teams (and implement directly when it’s the fastest/most effective path).

Skills

Required

  • hands-on threat detection and/or incident response experience
  • building detections
  • running investigations
  • improving operational playbooks
  • modern adversary tradecraft (TTPs)
  • practical detection strategies
  • response actions
  • threat modeling mindset
  • evaluating new infrastructure or features
  • identifying D&R implications
  • concrete requirements for teams shipping the system
  • Kubernetes/containerized environments
  • building detections from cluster telemetry
  • understanding common failure and attack modes
  • reasoning about lower-level infrastructure and datacenter risks
  • experience across major cloud platforms (Azure, AWS, GCP, OCI)
  • design cloud-agnostic detection approaches
  • building automation
  • scripting
  • using AI/agent tooling to accelerate investigations and automation

Nice to have

  • comfortable with scripting
  • enjoy using AI/agent tooling to accelerate investigations and automation—more “directing” than doing everything by hand

What the JD emphasized

  • emergent security concerns in a frontier AI lab environment
  • agents operating across infrastructure at scale
  • building automation that replaces repetitive D&R work
  • using agent-style workflows where they meaningfully reduce toil
  • thinking through how to detect and respond to agents operating across systems at scale