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. This role focuses on developing rich semantic signals from a variety of sources—including queries, creatives, metadata, and user interactions—to support scalable ad retrieval, creative ranking, and marketplace optimization. You'll work at the forefront of LLM fine-tuning, knowledge graph construction, semantic search, and multimodal representation learning to extract structured intelligence from unstructured data. While ad tech knowledge is a strong bonus, the core of the role is building high-quality, privacy-centric signals that fuel some of Apple’s most advanced machine learning systems. As part of the Ads Signals Intelligence team, you’ll be shaping the foundation of Apple’s ad ranking and relevance systems through world-class signal understanding. You’ll work on problems at the cutting edge of retrieval, multimodal learning, LLMs, and content intelligence—while contributing to Apple’s mission to deliver high-performing, privacy-first advertising experiences at scale.

Design, implement, and scale ML systems that extract high-value semantic signals from structured and unstructured content

Contribute to retrieval and ranking pipelines using techniques in query understanding, semantic embedding, and dense/sparse indexing

Fine-tune and apply Large Language Models (LLMs) for NLP tasks like content labeling, rewriting, and semantic similarity

Construct and utilize knowledge graphs and entity linking systems for enriching creative and query signals

Work with multimodal data (e.g., combining text, image, and metadata signals) to build robust, cross-domain signal representations

Build core components for a content understanding platform, such as entity extraction, topic modeling, creative summarization, and taxonomy generation

Own experimentation, offline evaluation, and online validation of signal pipelines at massive scale

Collaborate across engineering, infra, and product teams to productionize systems while meeting Apple’s high standards for reliability and privacy