Privacy-Preserving Engineer - Q

Our team is new, growing, and evolving quickly, and we are hiring across multiple disciplines. We work at the intersection of machine learning, hardware, and software, creating technologies that reshape how people communicate and experience the world around them in everyday life.

At Apple, we believe privacy is a fundamental human right, and we continuously innovate to deliver state-of-the-art machine learning capabilities while ensuring uncompromising, mathematically provable privacy guarantees.
We are looking for a Privacy Preserving Engineer who combines strong machine learning fundamentals with practical software engineering skills. This is an applied, hands-on engineering role: you will work closely with theoretical researchers to deeply understand their mathematical formulations, understand the core contributions of their work, and modify the heart of our training pipelines and models accordingly.

You do not need to come from a hyper-scale distributed infrastructure background—the focus here is on algorithmic implementation, deep understanding of ML mechanisms, modifying core training dynamics, and packaging and presenting complex privacy-preserving solutions cleanly to cross-functional partners and stakeholders.

Minimum Qualifications

M.Sc. in Computer Science, Electrical Engineering, Data Science, Applied Mathematics, or a related technical field
3+ years of hands-on experience developing, designing, and training machine learning and deep learning models
Strong software engineering foundation with advanced proficiency in Python and PyTorch, including deep familiarity with modifying internal training loops, custom optimizers, and gradient computation
Solid mathematical maturity with the ability to understand theoretical papers, algorithmic proofs, and privacy mechanisms from first principles
Demonstrated experience in privacy-preserving technologies (e.g., Differential Privacy, private aggregation, or privacy-budget accounting)
Strong technical presentation and communication skills in English, with the ability to effectively showcase technical prototypes to diverse audience

Preferred Qualifications

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