Internship - Privacy Preserving Software Engineer

Descrizione del lavoro:

Application Deadline - 31st January 2026 Privacy is a fundamental human right. At Apple, it's also one of our core values. We design Apple products to protect your privacy and give you control over your information. It's not always easy. But that's the kind of innovation we believe in. If you are the type of person that feels a personal stake in protecting privacy of users, join our Privacy Preserving Measurements and Machine Learning team. You will play a meaningful role in improving user privacy by building frameworks and algorithms using groundbreaking technology at every level of the technical stack. You will help product and infrastructure teams to ensure user data privacy is a core component in every feature that we ship.
You will design and implement features that learn crucial insights from hundreds of millions of devices while preserving user privacy. You will use your software engineering skills to build modular and well tested code to prototype cutting edge privacy preserving algorithms. You will benchmark your solution to show efficiency and scalability. Embedding with the core team, you will contribute to prototypes, experiments, or internal tools that support privacy-preserving machine learning and analytics. You will review designs and code by others and provide constructive feedback, while continuously learning from colleagues.
Pursuing a PhD or Master's degree in Computer Science, Engineering, Maths or a related technical field. Strong object-oriented software design and development skills. Proficiency in at least one programming language (e.g., Python, Swift, C++). Comfortable working independently to deliver results with minimal direction. Effective written and verbal communication skills, with the ability to explain technical ideas to a diverse audience.
* Experience with privacy-preserving technologies such as differential privacy, federated learning, multi-party computation, or trusted execution environments. * Familiarity with machine learning frameworks (e.g., PyTorch, TensorFlow) and production ML systems. * Demonstrated interest in developing practical, scalable solutions rather than purely theoretical systems