Staff Security Software Engineer, AI Security Engineering

Databricks is a Data and AI company whose AI Security Engineering team builds security tools, detection systems, and engineering infrastructure to protect its AI platform and customer AI capabilities. The Staff Security Software Engineer will set the technical direction, architecture, standards, and methodology for AI security engineering, while leading scalable threat detection, security assessment, cross-organizational technical alignment, and mentorship.

Responsibilities:

• 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
• 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 security red-teaming tooling: automated adversarial testing platforms that simulate how real attackers attempt to abuse Databricks' AI systems
• Serve as the technical authority on AI security engineering for the Product Security, SITH, IR, and ConMon teams — ensuring that AI security tooling outputs integrate cleanly into their workflows and meet their detection and assessment needs
• Represent AI Security Engineering in architecture reviews, platform security decisions, and cross-team technical discussions where AI security engineering considerations are material
• Establish AI security engineering standards that teams building AI-connected systems can adopt — reusable patterns for securing AI components in the Databricks platform
• Mentor senior and mid-level engineers on AI security engineering architecture, adversarial threat modeling, and technical leadership
• Lead design reviews, define team engineering practices, and drive continuous improvement in the quality and reliability of AI security tooling

Requirements:

• 7–10 years of experience in security software engineering, security engineering, or 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, scaled, and maintained
• Strong technical communicator: can align engineering and security leadership on architectural direction and drive cross-team adoption of standards and patterns
• 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