Privacy-Preserving Engineer - Q

Apple

2d

About The Position

Apple is hiring a Privacy-Preserving Engineer for a new, growing team working at the intersection of machine learning, hardware, and software. The role focuses on developing machine learning capabilities with mathematically provable privacy guarantees. It is an applied, hands-on engineering position that translates research into practical systems: the engineer works closely with theoretical researchers, understands mathematical formulations and research contributions, and modifies model training pipelines and models accordingly. The emphasis is on algorithmic implementation, understanding machine learning mechanisms, changing core training dynamics, and presenting privacy-preserving solutions to cross-functional partners and stakeholders, rather than hyper-scale distributed infrastructure.

Requirements

• Master’s degree in Computer Science, Electrical Engineering, Data Science, Applied Mathematics, or a related technical field.
• At least 3 years of hands-on experience developing, designing, and training machine learning and deep learning models.
• Strong software engineering foundation and advanced proficiency in Python and PyTorch, including deep familiarity with modifying internal training loops, custom optimizers, and gradient computation.
• Mathematical maturity to understand theoretical papers, algorithmic proofs, and privacy mechanisms from first principles.
• Demonstrated experience with privacy-preserving technologies, such as Differential Privacy, private aggregation, or privacy-budget accounting.
• Strong technical presentation and communication skills in English, including the ability to present technical prototypes to diverse audiences.

Nice To Haves

• Hands-on experience with open-source differential privacy libraries such as Opacus, OpenDP, Diffprivlib, or TensorFlow Privacy.
• Experience implementing DP-SGD, gradient clipping, noise-addition mechanisms, or Rényi DP accounting in deep-learning frameworks.
• Exposure to cryptographic privacy primitives such as Secure Multi-Party Computation (MPC), Federated Learning, or trusted execution environments.
• Experience balancing and optimizing privacy budgets against task performance on complex datasets.

Responsibilities

• Translate advanced mathematical theory, privacy mechanisms, and research formulations into robust, practical machine learning systems.
• Modify, inject, and optimize custom components, loss functions, clipping mechanisms, and private optimizers in PyTorch model-training loops.
• Design, architect, and implement end-to-end privacy-preserving machine learning pipelines.
• Evaluate privacy-utility trade-offs by measuring model accuracy, convergence behavior, and empirical privacy leakage.
• Collaborate with theoretical research scientists to validate algorithmic hypotheses and verify theoretical guarantees in practical setups.
• Package, document, and present research implementations, technical prototypes, and proof-of-concepts to cross-functional teams and leadership.
• Stay updated on modern privacy-enhancing technologies and open-source differential privacy tooling.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level