Staff Security Engineer - AI-Driven Security Assurance

Summary

Apple Services Engineering (ASE), the team behind iCloud and Media services and the infrastructure that powers them, is looking for a Staff Security Engineer to partner with engineering teams to build AI-driven security assurance capabilities. You will collaborate with developers, site reliability engineers, and security teams to protect ASE services and design a secure foundation for services at Apple. Your work will include scaling assurance activities, including security risk assessments, threat modeling, and development of security standards. You will work with partner teams in security engineering, privacy, and offensive security to keep Apple's services secure for our users. If you love diving into complex technical systems, sharing security improvements, and making security better, we want to hear from you!

Minimum Qualifications

10 or more years of hands-on experience in security assurance, with demonstrated depth in security architecture, threat modeling, security design reviews, and security risk assessment. Experience evaluating AI system performance, including reliability, correctness, failure, modes, and effectiveness against defined objectives. Proficiency in Python or a comparable programming language, API integration, and production software development. Experience with LLM orchestration, agent tool use, retrieval systems, context engineering, and techniques for grounding AI outputs in trusted organizational data. Experience incorporating security into spec-driven development, requirements engineering, secure design practices, or comparable structured software development methodologies.

Preferred Qualifications

Bachelor's degree or equivalent experience preferred Experience applying generative AI or agentic automation specific to security assurance, threat, modeling, architecture, reviews, vulnerability analysis, or spec-driven development. Experience building enterprise knowledge systems using RAG, knowledge graphs, persistent memory, or hybrid retrieval techniques. Experience in integrating AI agents with engineering tools, source code, repositories, architecture, documentation, specifications, requirements systems, and security assessment workflows. Experience establishing traceability between specifications, security requirements, implementation artifacts, tests, and assurance evidence.

Description

In this role, you will combine deep expertise in security architecture, threat modeling, security design review, and spec-driven development (SDD) with hands-on experience building sophisticated AI-powered systems. You will design and develop intelligent security assurance capabilities that leverage large language models, autonomous agents, and build organization–specific security context to identify and prevent meaningful security risk across complex systems and architectures. You will also help define how security is incorporated into SDD practices, ensuring that security requirements, threat models, abuse cases, trust boundaries, controls, and verification criteria are established early and carried through the specification, implementation, testing, and development lifecycle. This is a hands-on engineering role at the intersection of security assurance and applied-AI. You will partner closely with software engineers, architects, and product teams working at the forefront of agent technology, to understand emerging capabilities, influence how agentic systems are designed and developed, and integrate security throughout the software development lifecycle (SDLC).

Responsibilities

Architect, develop, and deploy AI-powered security assurance systems that automate and augment security design reviews, threat modeling, and architectural risk assessments Define and implement security practices for spec-driven development, including security requirements, abuse cases, threat models, trust boundaries, control objectives, and security acceptance criteria Integrate security analysis and verification into the SDD lifecycle so that security considerations are addressed during specification, design, implementation, testing, and change review Build agentic workflows capable of analyzing complex system architectures and specifications, reasoning about security threats, identifying potential vulnerabilities, and producing actionable security findings Implement advanced context-management approaches, including retrieval-augmented generation (RAG), persistent agent memory, and other techniques that enable AI systems to reason effectively within the organization's security environment Apply evaluation frameworks to measure the accuracy, completeness, reliability, and security effectiveness of AI-generated assessments and SDD outputs Integrate AI-driven assurance capabilities into engineering workflows and existing security infrastructure to expand security coverage without proportionally increasing manual review effort Continuously evaluate emerging AI models, agent frameworks, and engineering techniques, identifying opportunities to improve security assurance capabilities Partner with security engineers, architects, and engineering teams to translate complex security requirements and expert judgment into reusable, automated assurance capabilities