Senior iOS Software Engineer

Apple
Seattle, United States of America2025-12-18

About the job

Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Apple is committed to delivering exceptional features and prioritizing user privacy. The Privacy Preserving Measurement & Machine Learning team develops state-of-the-art privacy-enhancing technologies for privacy-preserving measurement and machine learning. We are seeking an exceptional candidate to join our team contributing to iOS.

Responsibilities

Collaborate with cross-functional engineers to design and develop end-to-end measurement systems that meet Apple's industry-leading privacy standards.

Operate independently to solve complex problems at the intersection of privacy-preserving technology, applied cryptography and machine learning.

Develop on-device data processing systems that enable training and evaluation of generative AI systems while preserving user privacy.

Qualifications

Minimum

Proficiency in one or more object-oriented programming languages such as C++, Objective-C, Swift.

Industry experience of iOS development, or experience of development on other embedded systems.

Proficiency in problem-solving, coupled with creativity in identifying effective solutions, and the ability to collaborate effectively within a cross-functional team.

2+ years of industry experience with a Bachelor’s degree or equivalent experience in Computer Science or a related technical field.

Preferred

Passion for protecting customer privacy.

Knowledge of basic privacy, security and cryptographic principles.

Experience with differential privacy, secure multi-party computation, trusted compute environment or private federated learning.

Ability to analyze system architecture and assess privacy & security impacts and suggest mitigations.

Experience with training, evaluating, and deploying machine learning models to production.