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 Have

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: 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 platforms that simulate how real attackers attempt to abuse Databricks' AI systems

Cross-Organizational Technical Leadership

• 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

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

Responsibilities

• Define 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, and integration patterns
• Own technical decisions on system scaling, integration with product security and detection pipelines, and build vs. buy vs. open-source tooling decisions
• Lead design and development of AI platform capabilities at production scale including behavioral analysis, prompt injection detection, and anomaly detection on agentic workflow behavior
• Define methodology for AI security assessment and continuous monitoring of AI capabilities
• Drive technical strategy for AI red-teaming tooling and automated adversarial testing platforms
• Serve as technical authority on AI security engineering for Product Security, SITH, IR, and ConMon teams
• Represent AI Security Engineering in architecture reviews and cross-team technical discussions
• Establish reusable AI security engineering standards for teams building AI-connected systems
• Mentor senior and mid-level engineers on AI security engineering architecture and adversarial threat modeling
• Lead design reviews and define team engineering practices