Security Engineer, Detection and Response

OpenAI OpenAI · AI Frontier · San Francisco, CA · 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, and partnering with other teams to ensure security requirements are met. A key aspect is evaluating and responding to emergent security concerns in a frontier AI lab, including strategies for agents operating at scale.

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

  • Threat detection
  • Incident response
  • Building detections
  • Running investigations
  • Improving operational playbooks
  • Modern adversary tradecraft (TTPs)
  • Threat modeling
  • Kubernetes/containerized environments
  • Cloud platforms (Azure, AWS, GCP, OCI)
  • Automation
  • Scripting
  • Clear communication
  • Collaboration

Nice to have

  • AI/agent tooling to accelerate investigations and automation

What the JD emphasized

  • emergent security concerns in a frontier AI lab environment
  • agents operating across infrastructure at scale
  • building detections from cluster telemetry
  • reasoning about lower-level infrastructure and datacenter risks
  • agent-style workflows where they meaningfully reduce toil
  • detect and respond to agents operating across systems at scale