Staff Security Software Engineer, AI Security Engineering

RDQ226R605; This role can be based remotely anywhere in the United States.

The AI Security Engineering team at Databricks builds the security tools, detection systems, and engineering infrastructure that protect Databricks' AI platform and the AI capabilities our customers depend on. We are the builders — designing and shipping security tooling that scales AI threat detection, automates security assessment of AI systems, and gives Databricks and its customers high-confidence assurance that AI capabilities are operating securely. We sit at the intersection of security engineering and AI systems: we understand how AI systems work, how they can be attacked, and how to build engineering solutions that keep them safe at scale.

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As a Staff Security Software Engineer on the AI Security Engineering team, you set the technical direction for AI security engineering at Databricks — defining the architecture, standards, and methodology by which the team builds tooling for AI security assessment, detection, and defense. You are recognized across the Security organization as the authority on AI security engineering: the person engineering leadership consults when AI security tooling decisions have organizational-scale consequences.

You operate across team and organizational boundaries — aligning the AI Security Engineering team's technical roadmap with the detection, GRC, and product security teams that depend on its outputs, and driving AI security engineering standards that the whole security organization can adopt.

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The Impact You Will HaveAI Security Engineering Architecture

• Define the architecture and technical strategy for Databricks' AI security tooling platform — spanning adversarial testing, behavioral monitoring, threat detection, and automated assessment of AI components
• Set engineering standards for the team: design review processes, reliability requirements, observability practices, security properties of the tooling itself, and integration patterns with downstream consumers
• Own the technical decisions on how the team's systems scale to cover Databricks' growing AI surface, how they integrate with product security and detection pipelines, and what tooling capabilities to build vs. buy vs. open-source

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, 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 plat