Senior Machine Learning Engineer - Knowledge Graph

We are the Knowledge Graphs team at Apple building the intelligence layer that connects Apple's media ecosystem. We construct the single source of truth for Apple's content metadata - modeling and canonicalizing millions of entities across Music, Books, and Podcasts into richly connected knowledge representations that power search, discovery, personalization, analytics, and editorial experiences across Apple Services. As part of Apple Service Engineering, we operate at global scale, blending art and technology to deliver extraordinary experiences across Apple Music, App Store, Apple TV, Apple Fitness+, Apple Podcasts, and Apple Books-serving hundreds of millions of users in 175+ countries and 37+ languages.

We're looking for an accomplished Machine Learning Engineer with a deep passion for Knowledge Graphs and Agentic AI systems to help shape how we understand and organize the world's creative works. If you're inspired by the challenge of transforming complex, messy data into connected, intelligent systems that can reason, adapt, and evolve-we'd love to work with you!

Description

At Apple, intelligence begins with connection - between data, ideas, and people.

Join us in building the Knowledge Graphs and Agentic AI systems that bring those connections to life, powering how millions experience the world's music, books, and podcasts!

As a lead-level AI/ML Engineer, you will drive the development and scaling of knowledge graph intelligence and agentic AI systems at Apple. You'll architect the models, pipelines, and reasoning frameworks that turn billions of metadata records into a cohesive, adaptive source of truth.

","responsibilities":"Design and implement machine learning and knowledge graph pipelines that model, link, and canonicalize data across multimodal sources.

Develop agentic AI systems that autonomously reason over structured and unstructured data-enabling self-correcting, context-aware entity intelligence.

Integrate LLMs, retrieval-augmented generation (RAG), and multi-agent frameworks to enhance semantic reasoning, metadata enrichment, and decision-making.

Collaborate closely with data science, infrastructure, and product partners to bring research ideas into production across Apple Music, Books, and Podcasts.

Mentor engineers and foster a culture of technical excellence and curiosity.

Contribute to the long-term roadmap for Apple's Knowledge Graph and Agentic AI ecosystem, helping shape how intelligence powers user experiences at scale.

Preferred Qualifications

Experience with distributed ML frameworks such as Ray, large-scale data pipelines, feature engineering systems.

Familiarity with multimodal learning, ontology management, or data governance.

Proven ability to align AI innovation with product and user impact.

Passion for human-centered AI that balances creativity, privacy, and intelligence.

Curiosity about emerging paradigms in self-organizing AI systems and autonomous knowledge representation.

Minimum Qualifications

10+ years of experience in machine learning or applied AI, including at least 2+ years in a technical or team lead role.

Proven success leading end-to-end ML projects from research through deployment.

Demonstrated experience in supervised and RL model training, fine-tuning, and distillation.

Demonstrated experience with Agentic AI systems - building multi-agent workflows, LLM-based orchestration, or autonomous reasoning pipelines.

Demonstrated experience in Knowledge Graph construction, entity resolution, or semantic reasoning.

Strong expertise in ML frameworks such as PyTorch, Hugging Face, LangGraph, or equivalent.

Strong foundation in deep learning, NLP, and Generative AI (fine-tuning, RAG, and prompt-based orchestration).

Deep proficiency in Python, with working knowledge of Java, Scala, or Go.

Excellent communication and cross-functional collaboration skills.

M.S. or Ph.D. in Computer Science, Machine Learning, or related technical field.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .