Staff Security Software Engineer, Agentic Security Engineering

About this role

RDQ226R605 This role can be based remotely anywhere in the United States. About Databricks Databricks is the data and AI company. More than 12,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow.

About the Team The Agentic Security Engineering team is a horizontal, shared services engineering team that makes every Databricks security function AI-native — building the AI agents, "personas," and shared platform that security teams across the org (Detection & Response, Threat Intelligence, Vulnerability & Product Security, Red Team, GRC, and Continuous Monitoring) build on to operate at machine speed, safely and reliably.

We leverage AI to transform how security is done. The Role As Staff Security Software Engineer , you own a major technical workstream designing and shipping production-grade AI agents and the platform capabilities that power them, with reliability, observability, and safe-by-default security as first-class requirements. You set the standards for how agents are built and operated and co-build with partner security teams.

Example use cases you'll enable, led by AI threat detection at scale (alert triage and false-positive reduction, fleet-scale anomaly detection), extend to threat hunting, vulnerability management, product security, red team/offensive testing, and GRC/assurance use cases. The Impact You Will Have Agentic Security Engineering Architecture Design, ship, and operate production AI agents/personas that security teams depend on, starting with AI threat detection at scale.

Build and harden the shared platform so any security team can ship agents safely and reliably — i.e., sandboxing, scoped least-privilege identity, observability, and a reusable agent/tool catalog. Set engineering standards and make reliability first-class — design review bar, latency/cost optimization, monitoring, clean CI-based deploys, and safe model upgrades. Establish AI quality and evaluation practices: eval frameworks, LLM-as-judge (with bias controls), and regression gates that catch quality drops before production.