Staff Security Software Engineer, Agentic Security Engineering

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.

AI Threat Detection at Scale

• Lead the design and development of AI platform capabilities that operate at production scale — behavioral analysis of usage, detection of prompt injection attempts, and anomaly detection on agentic workflow behavior.
• Define the methodology for AI security assessment: how Databricks systematically evaluates new AI capabilities against a comprehensive threat model before deployment and monitors them continuously after.
• Drive technical strategy for AI red-teaming tooling: automated adversarial testing platforms that simulate how real attackers attempt to abuse Databricks' AI systems.

Cross-Organizational Technical Leadership & Mentorship

• Co-build with and enable partner teams across the security org, and mentor mid-level engineers.
• Lead design reviews, define team engineering practices, and drive continuous improvement in the quality and reliability of AI security tooling.

What We Look For

• Demonstrated experience building, shipping, and operating production AI agents that others depend on, with an intentional pivot to AI-native transformation of a function at scale (prototypes and demos don't count).
• Expert-level Python for production systems at scale.
• Depth across the production-AI dimensions: agent architecture & orchestration; context & memory management; production operations (latency, cost, caching, monitoring, model upgrades, reliability); and AI quality & evaluation (eval frameworks, LLM-as-judge, regression testing).
• Hands-on experience with modern agentic frameworks and LLM tooling (e.g., LangChain, Hugging Face, frontier models) and MCP-style tool integration.
• Strong technical communicator who aligns partners and drives adoption without direct authority.
• 7+ years in software or security engineering, with demonstrated technical leadership on a significant workstream.
• Track record of shipping high-quality security tooling that other teams depend on in production.