Original listing text, shown exactly as published by the company.
About the Role
As a Security Engineer on Detection & Response, you’ll help protect OpenAI’s most sensitive assets– including our intellectual property, customer data, and the infrastructure that supports them– by building and operating the systems we use to detect suspicious activity and respond effectively when it matters. You’ll work across endpoints, identity, cloud, hyperscale compute infrastructure, and datacenter-adjacent layers, partnering closely with security teams and infrastructure owners to define the telemetry and response requirements we need and building tooling and automation where it delivers the most leverage.
In this role, you will
- 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.
- Engineer detection pipelines and tooling: develop rule lifecycle management, measurement/quality loops (coverage, precision, latency), tuning processes, and safe rollout patterns.
- Automate response and investigations by building workflows that reduce toil (triage, enrichment, containment, evidence capture) and improve time-to-understand/time-to-contain.
- 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.
- 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).
- Evaluate and respond to emergent security concerns in a frontier AI lab environment, such as detection and response strategies for agents operating across infrastructure at scale.
You might thrive in this role if you
- Have hands-on threat detection and/or incident response experience, including building detections, running investigations, and improving operational playbooks.
- Understand modern adversary tradecraft (TTPs) and can translate it into practical detection strategies and response actions.
- Bring a threat modeling mindset. You can evaluate new infrastructure or features, identify D&R implications (what could go wrong, what we’d need to see, how we’d respond), and turn that into concrete requirements for teams shipping the system.
- Have experience working in Kubernetes/containerized environments, including building detections from cluster telemetry and understanding common failure and attack modes (workloads, nodes, control plane, networking).
- Are comfortable reasoning about lower-level infrastructure and datacenter risks, such as firmware/BMC surfaces, network segmentation/telemetry, and hard-to-observe control paths.
- Have experience across major cloud platforms (Azure, AWS, GCP, OCI), and can design cloud-agnostic detection approaches where possible.
- Like building automation that replaces repetitive D&R work, including thoughtfully using agent-style workflows where they meaningfully reduce toil, while keeping outcomes measurable, auditable, and safe.
- Are energized by new problem areas at a forward-leaning technology company: e.g., thinking through how to detect and respond to agents operating across systems at scale, and turning those ideas into pragmatic telemetry and response requirements.
- Communicate clearly and collaborate well across teams. You can translate D&R needs into clear requirements, align stakeholders, and drive follow-through across technical and non-technical audiences.
- Are comfortable with scripting and enjoy using AI/agent tooling to accelerate investigations and automation—more “directing” than doing everything by hand.
