Staff Security Software Engineer, AI Security

• As a Staff Security Software Engineer on the AI Security team, you are a senior technical leader who sets the standards for how Databricks secures its AI and ML capabilities.
• You lead AI red team engagements against production AI systems, conduct security architecture reviews for complex, multi-system AI features, and build the tooling and frameworks that scale the team's impact.
• You are a subject matter expert in at least two AI security domains and you operate with significant autonomy- driving cross-team remediation, setting technical standards, and mentoring teammates in both offensive techniques and secure AI design.

Requirements

• 7–10 years of combined experience in offensive security, AI/ML security research, or product security engineering, with demonstrated leadership in securing complex systems
• Subject matter expert in at least two of the following AI security domains:
• LLM and generative AI security (prompt injection, jailbreaking, training data extraction)
• AI agent and orchestration security (MCP, memory sharing, multi-agent systems)
• ML infrastructure and serving security (model serving multi-tenancy risks, training infrastructure security)
• AI data governance and privacy (fine-grained access control, data residency, inference data isolation)
• Demonstrated ability to design and execute adversarial attacks against production AI systems
• Deep understanding of AI/ML platform architecture- how models are trained, served, and integrated, and where the trust boundaries between components lie
• Expert in at least one major cloud platform (AWS, Azure, GCP) and its AI/ML security model
• Proficient in Python; able to read and analyze ML model code, training scripts, and API serving code; working knowledge of at least one additional language (Go, Java, Scala, Rust)
• Track record of driving cross-team AI security improvements and influencing product architecture decisions
• Experience building automated security tooling for AI systems
• Strong communicator- translates AI security risks into actionable guidance for engineers, product managers, and leadership
• Pragmatic approach to risk- distinguishes real-world exploitable AI risk from theoretical concerns.