Privacy-Preserving Machine Learning Researcher - 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 our research teams are dedicated to building technologies that deliver intelligent capabilities with rigorous, provable privacy guarantees. We are looking for a research scientist with an extraordinary theoretical background in machine learning and computational mathematics. This is a foundational theory role: we are seeking someone who truly understands how learning algorithms function from first principles, identifies computational and statistical barriers, and proves what is achievable under information-theoretic constraints. While knowledge of privacy mechanisms (such as Differential Privacy) and cryptography is a strong advantage, the core priority is deep theoretical maturity in statistical learning, sample complexity, and optimization theory. You will bridge mathematical rigor with practical algorithmic innovation, shaping the next generation of private machine learning at Apple.

Conduct foundational theoretical research into the limits, bounds, and trade-offs of learning algorithms under privacy, computational, and statistical constraints Formulate and prove theoretical guarantees for machine learning models, analyzing sample complexity, generalization error, and convergence rates Design, analyze, and benchmark novel privacy-preserving learning algorithms (e.g., DP-SGD, private aggregation, and local/central differential privacy paradigms) Collaborate with applied research and engineering teams to translate theoretical insights and mathematical proofs into practical, scalable machine learning solutions Explore intersections between statistical learning theory, optimization landscapes, and cryptographic primitives Author high-impact research papers for publication in top-tier machine learning and theoretical computer science conferences Stay at the forefront of foundational ML theory and privacy literature, identifying theoretical bottlenecks and proposing novel avenues of investigation

Ph.D. in Computer Science, Applied Mathematics, Statistics, Electrical Engineering, or a related theoretical/quantitative field Deep theoretical foundation in statistical learning theory, computational complexity, and optimization (e.g., sample complexity, PAC learning, empirical process theory, or convex/non-convex optimization) Proven track record of high-impact theoretical research and mathematical proof generation, demonstrated by publications in top-tier venues (e.g., COLT, NeurIPS, ICML, ICLR, STOC, FOCS, or IEEE TIT) Strong mathematical intuition and the ability to rigorously analyze the theoretical behavior and fundamental limits of algorithms Hands-on ability to prototype, validate, and simulate algorithmic concepts using Python Excellent written and verbal communication skills in English, with the ability to convey complex mathematical theory to both theoretical peers and applied teams

Domain background or prior research in Differential Privacy (DP), DP-SGD, private PAC learning, or privacy budget accounting Familiarity with cryptographic concepts and protocols (e.g., Secure Multi-Party Computation / MPC, Zero-Knowledge Proofs / ZKP, or Homomorphic Encryption) Experience bridging theoretical bounds with empirical deep learning models and large-scale training dynamics Understanding of information theory, concentration of measure, or high-dimensional statistics