Job description

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. 4+ years of experience in machine learning or applied research, with a focus on retrieval, ranking, NLP, or content understanding Deep understanding of information retrieval, semantic search, and query-document matching Strong hands-on experience with LLM fine-tuning, knowledge graph construction, and entity-centric modeling Experience working with multimodal models, including text, vision, metadata, or audio-based representations Proficiency in Python, and experience with one or more of ML frameworks like PyTorch, TensorFlow Background in statistical modeling, optimization, and ML theory Exposure to ad tech domains such as auction modeling, targeting, attribution, or creative optimization is a plus Demonstrated ability to deliver high-impact ML solutions in production environments Bachelor's in Computer Science, Machine Learning, Information Retrieval, NLP, or a related field. 7+ years of experience in machine learning or applied research, with a focus on retrieval, ranking, NLP, or content understanding MS or PhD in Computer Science, Machine Learning, Information Retrieval, NLP, or a related field.

Requirements

Must-haves

4+ years of experience in machine learning or applied research, with a focus on retrieval, ranking, NLP, or content understanding
Deep understanding of information retrieval, semantic search, and query-document matching
Strong hands-on experience with LLM fine-tuning, knowledge graph construction, and entity-centric modeling
Experience working with multimodal models, including text, vision, metadata, or audio-based representations
Proficiency in Python, and experience with one or more of ML frameworks like PyTorch, TensorFlow
Background in statistical modeling, optimization, and ML theory
Demonstrated ability to deliver high-impact ML solutions in production environments
Bachelor's in Computer Science, Machine Learning, Information Retrieval, NLP, or a related field.
7+ years of experience in machine learning or applied research, with a 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

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.
Exposure to ad tech domains such as auction modeling, targeting, attribution, or creative optimization is a plus

Application prep

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Keywords to mirror

machine learningmlllmpython

Platform: apple
Effort: medium

Benefits

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