Staff Product Security Engineer

AI 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 including design review processes, reliability requirements, observability practices, security properties of the tooling, and integration patterns.
• Own technical decisions on scaling systems to cover growing AI surface, integration with product security and detection pipelines, and build vs. buy vs. open-source choices.

AI Threat Detection at Scale

• Lead design and development of AI platform capabilities at production scale including behavioral analysis, detection of prompt injection, and anomaly detection on agentic workflows.
• Define methodology for AI security assessment: systematic evaluation of new AI capabilities against threat models before deployment and continuous monitoring after.
• Drive technical strategy for AI red-teaming tooling: automated adversarial testing platforms simulating real attacker abuse of AI systems.

Cross-Organizational Technical Leadership

• Serve as technical authority on AI security engineering for Product Security, SITH, IR, and ConMon teams, ensuring tooling integrates into their workflows.
• Represent AI Security Engineering in architecture reviews, platform security decisions, and cross-team discussions.
• Establish AI security engineering standards and reusable patterns for teams building AI-connected systems.

Mentorship & Team Capability

• 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 quality and reliability of AI security tooling.

Requirements

• 7–10 years of experience in security software engineering, security engineering, or 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 integration into security operations.
• Deep expertise in AI/ML security including adversarial ML, prompt injection, model security, agentic framework trust boundaries at both research-informed and engineering-practical levels.
• Experience designing security tooling architectures that span multiple teams and systems, defining platform structure, scale, and maintenance.
• Strong technical communicator able to align engineering and security leadership on architectural direction and drive cross-team adoption.
• Track record of shipping high-quality security tooling that other teams depend on in production.

Nice to Have

• 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.