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. --- 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 [... source excerpt omitted ...] versarial 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 [... source excerpt omitted ...] a closely related discipline; with demonstrated technical leadership of security tooling programs and organizational-level impact Expert Python engineering: designs and delivers production systems at scale; understands observability, reliability engineering, and how security tooling integrates into larger security operations ecosystems Deep expertise in AI/ML security — adversarial ML, prompt injection, model security, agentic framework trust boundaries — at both a research-informed and engineering-practical level Experience designing security tooling architectures that span multiple teams and systems — not just building features, but defining how the platform is structured, sc