Security Engineer, Detection and Response - Emea

OpenAI OpenAI · AI Frontier · London, United Kingdom · Security

Security Engineer focused on Detection and Response for OpenAI's infrastructure, products, and research environments. The role involves building and operating systems to detect suspicious activity, automate response, and partner with other teams to ensure security requirements are met. It also includes evaluating and responding to emergent security concerns in a frontier AI lab, with a specific mention of agents operating across infrastructure 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

  • 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
  • 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
  • using agent-style workflows
  • measurable, auditable, and safe outcomes
  • new problem areas at a forward-leaning technology company
  • detect and respond to agents operating across systems at scale
  • pragmatic telemetry and response requirements
  • communicate clearly
  • collaborate well across teams
  • translate D&R needs into clear requirements
  • align stakeholders
  • drive follow-through
  • scripting
  • using AI/agent tooling to accelerate investigations and automation

Nice to have

  • firmware/BMC surfaces
  • network segmentation/telemetry
  • hard-to-observe control paths

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
  • thoughtfully using agent-style workflows where they meaningfully reduce toil