About The Position

At Apple, we focus deeply on our customers' experience. Apple Ads brings this same approach to advertising, helping people find exactly what they're looking for and helping advertisers grow their businesses! Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, and MLS Season Pass. Everything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes-from small app developers to big, global brands. Because when advertising is done right, it benefits everyone! Apple's Ads Signals Intelligence team is seeking a hands-on and experienced Machine Learning Engineer to develop the next generation of ML-driven signal platforms that power retrieval, prediction, and relevance across Apple's advertising ecosystem-including the App Store and Apple News. This role focuses on building content understanding systems and large-scale infrastructure capable of delivering near real-time signal updates, enabling smarter, privacy-aware decision-making throughout the ad delivery stack.

Requirements

  • 7+ years of experience in machine learning or applied research.
  • Focus on retrieval, ranking, NLP, or content understanding.
  • MS or PhD in Computer Science, Machine Learning, Information Retrieval, NLP, or a related field.

Nice To Haves

  • Ad tech knowledge.

Responsibilities

  • Develop rich semantic signals from a variety of sources including queries, creatives, metadata, and user interactions.
  • Support scalable ad retrieval, creative ranking, and marketplace optimization.
  • Work on LLM fine-tuning, knowledge graph construction, semantic search, and multimodal representation learning.
  • Extract structured intelligence from unstructured data.
  • Shape the foundation of Apple's ad ranking and relevance systems through world-class signal understanding.
  • Contribute to Apple's mission to deliver high-performing, privacy-first advertising experiences at scale.
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