Internship - Privacy Preserving Machine Learning Engineer

Application Deadline - Monday 3rd November 2025 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 significant 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.

Description

You will design and implement features on a privacy preserving platform that protects privacy of hundreds of millions of devices. You will use your software engineering skills to build modular and well tested code to prototype groundbreaking privacy preserving algorithms. You will work with IETF working groups and research community to prove the robustness of your implementation. You will benchmark your solution to show efficiency and scalability. Embedding with the core team, you’ll work with other software/ML engineers and researchers. You will review designs and code by others and provide constructive feedback, while continuously learning from colleagues.

Minimum Qualifications

Strong skills in object-oriented software design, developing and testing. Proficient in at least one programming language, preferably Python. Proficient in common machine learning and deep-learning frameworks such as PyTorch, Tensorflow. Contributions to open source code a huge bonus. Comfortable working independently to deliver results with minimal direction. Excellent problem solving, critical thinking, and communication skills. Oriented towards practical solutions that can be proven empirically, as opposed to theoretical research.

Preferred Qualifications

Background in Privacy, Federated Learning (FL), Multi-Party Computation, Trusted Compute is a huge plus