Job Overview:
This role builds experimentation platforms empowering Apple engineers to deliver ML-driven user experiences across 2+ billion devices, requiring systems-level programming in Objective-C/Swift to develop frameworks and daemons while optimizing performance and scalability. You’ll collaborate with ML engineers to implement features for internal experimentation systems across all Apple OSes, maintaining code and contributing to architectural decisions that directly impact flagship products. The position demands deep iOS/macOS framework knowledge, production-grade software delivery skills, and familiarity with Xcode/Instruments, with bonus consideration for candidates experienced in large-scale systems, API design, or understanding platform design’s user experience implications.
>> View full job details on Apple’s official website.
Resume and Interview Tips:
When crafting your resume for this Apple AI/ML OS Engineer role, foreground your systems programming depth with specific metrics around Objective-C/Swift projects – quantify device scale, performance improvements, or framework adoption rates. Highlight any iOS/macOS daemon/framework work with concrete examples like ‘Developed [X] framework deployed to [Y] million devices, improving [Z] metric by 15%.’ For ML/experimentation experience, emphasize practical implementation over theoretical knowledge – describe A/B testing systems you’ve built or ML model deployment pipelines. Apple values polished communication, so ensure your resume demonstrates clarity through well-structured bullet points and precise technical terminology. Include a ‘Key Contributions’ section showcasing projects where your work directly improved scalability or reliability at substantial scale.
Prepare for behavioral questions probing how you’ve balanced innovation with stability in OS-level development – have 2-3 stories ready about shipping complex systems software under tight deadlines. Expect deep technical discussions on memory management, concurrency patterns, and power optimization in iOS environments; review Apple’s WWDC videos on these topics. For system design portions, practice whiteboarding experimentation architectures that handle billions of data points while maintaining user privacy – Apple prioritizes designs that scale elegantly without compromising performance. When discussing ML, focus on practical integration challenges like model quantization for edge devices rather than algorithm theory. Demonstrate your platform mindset by asking insightful questions about how evaluation frameworks interact with other Apple services like Core ML or Create ML.