Apple is looking to build the intelligence layer that connects Apple’s media ecosystem by constructing a single source of truth for Apple’s content metadata. This involves modeling and canonicalizing millions of entities across Music, Books, and Podcasts into richly connected knowledge representations to power search, discovery, personalization, analytics, and editorial experiences across Apple Services.
Requirements
- Deep proficiency in Python, with working knowledge of Java, Scala, or Go.
- Strong expertise in ML frameworks such as PyTorch, Hugging Face, LangGraph, or equivalent.
- Proven experience in Knowledge Graph construction, entity resolution, or semantic reasoning.
- Hands-on experience with Agentic AI systems - building multi-agent workflows, LLM-based orchestration, or autonomous reasoning pipelines.
- Strong foundation in deep learning, NLP, and Generative AI (fine-tuning, RAG, and prompt-based orchestration).
- Experience with large-scale data pipelines, distributed model training, or feature engineering systems.
- Familiarity with multimodal learning, ontology management, or data governance.
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.
Other
- 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.
- Excellent communication and cross-functional collaboration skills.
- M.S. or Ph.D. in Computer Science, Machine Learning, or related technical field.
- Proven ability to align AI innovation with product and user impact.
