Sr. Privacy Systems Software Engineer, Sensing & Connectivity

Apple's privacy engineering team is responsible for designing and enforcing the controls that keep user data protected across every Apple platform. As generative AI and intelligent features reshape how people interact with technology, the expectations for privacy engineering are evolving rapidly. We are looking for exceptional systems engineers who want to define how privacy works in an AI-driven world -- ensuring that powerful new capabilities never come at the cost of user trust.

You will design, implement, and debug production systems in C, Objective-C, and Swift that power privacy controls across Apple platforms (iOS, iPadOS, macOS, watchOS, visionOS, Apple TV, and HomePod). Your work will span architecting permission frameworks, strengthening privacy architecture against evolving threat vectors, and building foundational components that operate across operating systems.\n\nA critical part of this role is tackling the new privacy challenges introduced by generative AI technologies. You will work on problems like controlling how on-device models and intelligent features access sensitive user data; designing permission and consent frameworks for AI-driven workflows; enforcing data minimization and sandboxing for on-device AI models; and building privacy-preserving on-device inference into our systems from the ground up. These are emerging challenges at the forefront of the industry.

Proficiency in C, Objective-C, C++, or Swift\nExperience designing system-level APIs and background daemons\nStrong Unix-like systems programming knowledge\nExperience building reliable daemons with robust lifecycle management\nExperience designing fault-tolerant error handling for long-running system daemons\nClear technical communication skills

5+ years professional software engineering experience\n- System architecture design experience for privacy or security software\n- Cross-functional technical leadership experience\n- macOS or Unix systems programming background\nFull software development lifecycle experience\nRelational database and local storage design familiarity\nPractical experience using generative AI tools in an engineering workflow (e.g., AI-assisted code generation, LLM-powered debugging)\nAbility to reason about the privacy and data-access implications of machine learning systems at the systems level\nExperience with responsible AI development practices, including AI safety, privacy-preserving machine learning, or designing guardrails and safety mechanisms for intelligent features