Runway ML Detection & Response Engineer at Runway ML to own detection and response for security incidents across infrastructure and research environments. Build automated detection and response systems.
Responsibilities
Open to hiring remote — we also have offices in New York, San Francisco, Seattle, London, Paris, and Tel Aviv.
Runway is hiring a Detection & Response Engineer to own how we find and stop attacks against our infrastructure, our research environment and our products.
Securing a company that trains and serves frontier video models is a different problem from securing a typical SaaS product. The environment includes research compute, large training datasets, a fast-moving build pipeline and engineers who work inside AI-assisted tooling every day. Each of those changes what an attack looks like and what you need to see to catch it.
You'll join the Security team and build our detection and response program the way an engineer would: detections as code, response as automation, evidence as something you can query. This is a role with real ownership: you'll own the detection program end to end, from what we log to how quickly we close an incident. You'll report to the head of security, partner with engineers across platform and research, and shape how this function grows from here.
Own detection and response end to end: what we log, what we alert on, how we triage and how we recover
Write and tune detections as code across multiple cloud environments, Kubernetes, identity systems, endpoints and SaaS, and measure them on coverage and precision rather than alert volume
Lead incident response from the first alert through containment and forensics, then write the post-incident review people actually read
Build automation that takes toil out of triage, including enrichment, correlation, containment actions and evidence collection, and use LLM-based tooling where it holds up under audit
Monitor AI agents and developer tooling operating inside our environment, and turn that into concrete telemetry and controls
Partner with platform and research engineers so new systems ship with logging and response playbooks in place on day one
Run threat hunts and tabletop exercises against the parts of the environment that worry you most, and fix what you find
Turn incident and detection metrics into evidence for SOC 2, ISO 27001 and enterprise customer security reviews, working with our GRC team
Qualification
Hands-on incident response experienceComfort writing PythonClear writingJudgment about what to alert onEven better if you haveCloud forensics experiencePublished open source detection content
Required
Hands-on incident response experience: you've triaged live alerts, led investigations and written up what happened afterward
Experience building and tuning detections in a modern SIEM, ideally managed as code
Working knowledge of how attackers move through cloud and Kubernetes environments (IAM abuse, container escape, credential theft, supply chain compromise) and what that leaves behind in logs
Comfort writing Python, Typescript, Rust or another language to automate response work and connect security tools
Familiarity with at least one major cloud platform and with Kubernetes at the level of audit logs, RBAC and workload identity
Clear writing. Incident timelines, detection documentation and updates to leadership are all part of the job
Judgment about what to alert on, what to automate and when to wake someone up
Even better if you have
Experience monitoring GPU or HPC-style infrastructure, or research environments with large datasets
Experience building detections or guardrails for AI agents, LLM tooling or MCP servers
Cloud forensics experience: disk and memory acquisition, cloud audit trail reconstruction, chain of custody
