Machine
Learning and AI

Builds the data foundations that power Apple Intelligence, developing synthetic data pipelines, running global-scale collection operations, and pioneering human evaluation methods for generative AI and multimodal models.

Key areas:

Synthetic Data Generation, Data Collection, User Studies, Large-Scale Global Operations, Human Judgments and Data Validation, Generative AI and Multimodal Large Language Modelling, Computer Vision and Data Quality Assurance

Responsible for teaching Apple devices to see and understand the visual world, building everything from real-time image processing to multimodal foundation models that reason about what they see.

Key areas:

Data Science, Deep Learning, Multimodal Foundation Modelling, Visual Reasoning, Hardware-Aware Model Optimisation and Real-Time Computer Vision

Forms the data backbone of Apple Intelligence, including the infrastructure powering Siri and search as well as the experimentation platforms that measure what works.

Key areas:

Statistical Analysis, Data Insights, Evaluation, Experimentation, Machine Learning Applications, Research and Data Processing

Creates Apple’s frontier foundation models — from pre-training through post-training, evaluation and deployment — spanning the full model life cycle.

Key areas:

Deep Learning, Reinforcement Learning, Large Language Modelling, Vision Language Modelling, Multimodal Sensors, Retrieval-Augmented Generation, Agentic AI, Computer Vision, Natural Language Processing and Research

Keeps Apple’s most ambitious AI programs on track, orchestrating execution across foundation models, privacy infrastructure, search and evaluation.

Key areas:

Platform and Infrastructure, Foundation Models, Privacy and Compliance, Search and Knowledge, and Evaluation

Applies AI and machine learning to transform how Apple operates at enterprise scale, building intelligent platforms for everything from advanced conversational experiences to developer productivity.

Key areas:

Data Science, DevOps, Applied Machine Learning, User Experience, AI Platform Engineering, Robotic Process Automation, Security and Fraud Detection, Large Language Models, and Machine Learning Platform

Builds the systems that support AI and machine learning at Apple, optimising for performance, efficiency and scale across the full machine learning stack, influencing every model.

Key areas:

Machine Learning Framework, Platform Development, Distributed System Design and Operations and Accelerated Computing

Ensures Apple devices understand where they are in the world and how they move through it, building real-time, low-power algorithms for spatial tracking, 3D vision and scene understanding.

Key areas:

Real-Time Spatial Tracking, Navigation, 3D Computer Vision, Mapping, Localisation, Scene Understanding, Position Estimation, Multimodal Sensors and Structure from Motion

Transforms Apple’s foundation models into the language and speech experiences people use, centring on prompt engineering, model adaptation and product integration.

Key areas:

Model Development and Improvement, Research and Innovation, Data Analysis and Preparation, Product Integration, Performance Evaluation and Tuning, and Privacy and Ethics

Works at the frontier of privacy-preserving AI, developing techniques like differential privacy and building evaluation frameworks that ensure Apple Intelligence delivers world-class quality without compromising users’ trust.

Key areas:

Differential Privacy, Privacy and Data Analysis, Regulatory Readiness, Compliance Controls and Policy Frameworks

Defines how Apple navigates generative AI challenges, from model alignment and red teaming to safety mitigation and post-ship monitoring.

Key areas:

Risk Assessment, Product Feature Policy, Model Alignment, Technical Safety Mitigation, Engineering Solutions, Design Solutions, Evaluation, Red Teaming, and Post-Ship Monitoring and Response

Builds the search, knowledge graph and retrieval systems that connect people with the right information across Apple’s ecosystem, increasingly powered by conversational AI, retrieval-augmented generation and agentic capabilities.

Key areas:

Conversational and Agentic AI, Knowledge Graph, Large Language Modelling, Natural Language Processing, Post-Training, Retrieval-Augmented Generation, Information Retrieval, Reinforcement Learning and Summarisation

Develops defences against emerging threats at unprecedented scale by combining machine learning security research with platform and product security engineering to protect Apple’s systems and its customers.

Key areas:

Applied Machine Learning Security Research, Platform Security Engineering and Product Security