Senior Machine Learning Engineer, Media Knowledge

Do you want to help Apple deeply understand the music, shows, podcasts, and artists our customers love? Our team builds the intelligence behind Apple’s media experiences. We combine speech, language, vision, and knowledge technologies to understand audio, video, text, and images at the scale of Apple Music, Apple Podcasts, Apple TV, and Apple Sports. We build user-facing features powered by media intelligence, and our work also improves search, discovery, recommendations, and answers across Apple’s products. We’re a growing team in Barcelona, working with partners across Apple.

Description

We’re looking for a Senior Machine Learning Engineer to help shape how Apple represents media knowledge and uses it to build rich, accurate, and personal experiences. You’ll work at the intersection of large language models, information extraction, and knowledge graphs. You’ll have end-to-end ownership of designing how knowledge is modeled, building the systems that produce it at scale, and ensuring its quality. You’ll work closely with engineers and product partners, owning hard problems from early prototypes through to production.

We’re looking for someone with a healthy impatience: eager to prototype and test ideas with real data, iterating quickly and learning from what doesn’t work, while keeping a high bar for engineering and data quality. Above all, you’re curious and enjoy open problems where the path isn’t yet defined.

Minimum Qualifications

B.S. in Computer Science, Machine Learning, Engineering, or related field, or equivalent practical experience. Substantial experience building and shipping large-scale ML/AI systems in production. Hands-on experience building production ML systems using LLMs, ideally for information extraction, entity linking or knowledge graphs. Strong programming skills, and experience building production streaming and batch data pipelines. Excellent communication and collaboration skills, with the ability to explain complex topics clearly.

Preferred Qualifications

M.S. or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or related field. Experience designing knowledge representations, data models, or ontologies. Experience with multimodal understanding of audio, video, or images, such as speech recognition or video understanding. Experience with LLM-as-judge evaluation and running human annotation programs. Experience with vector search technologies, RAG, or LLM agents that use tools and multi-step reasoning. Experience with Spark or similar distributed data processing frameworks.