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Description
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. A 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.
Minimum Qualifications
Proficiency in C, Objective-C, C++, or Swift Experience designing system-level APIs and background daemons Strong Unix-like systems programming knowledge Experience building reliable daemons with robust lifecycle management Experience designing fault-tolerant error handling for long-running system daemons Clear technical communication skills Familiarity with AI-powered tools and the ability to incorporate them effectively into day-to-day workflows is required.
Preferred Qualifications
5+ years professional software engineering experience - System architecture design experience for privacy or security software - Cross-functional technical leadership experience - macOS or Unix systems programming background Full software development lifecycle experience Relational database and local storage design familiarity Practical experience using generative AI tools in an engineering workflow (e.g., AI-assisted code generation, LLM-powered debugging) Ability to reason about the privacy and data-access implications of machine learning systems at the systems level Experience with responsible AI development practices, including AI safety, privacy-preserving machine learning, or designing guardrails and safety mechanisms for intelligent features
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