Staff Security Software Engineer, Agentic Security Engineering

Databricks is a data and AI company that provides the Databricks Data Intelligence Platform. The Staff Security Software Engineer will design, ship, and operate production-grade AI agents and shared platform capabilities for security teams, with a focus on AI threat detection, reliability, observability, and safe-by-default security. The role also provides technical leadership, establishes engineering standards, and mentors engineers across the security organization.

Responsibilities:

• 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
• 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
• 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

Requirements:

• This role can be based remotely anywhere in the United States
• 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
• Research contributions or deep familiarity with adversarial ML, AI safety, or AI red-teaming methodology
• Experience with MLOps platforms, AI serving infrastructure, or AI platform security at cloud scale
• Familiarity with AI governance standards (NIST AI RMF, ISO/IEC 42001, EU AI Act technical provisions) as they apply to security engineering
• Open-source contributions or publications in AI security, adversarial ML, or security tooling