Machine Learning Engineer – Ads Signals Intelligence & Information Retrieval
3 Months ago • All levels • $143,100 PA - $264,200 PA
Research Development
Job Description
Apple is seeking a hands-on and experienced Machine Learning Engineer to develop the next generation of ML signal platforms for retrieval, prediction, and relevance within Apple's advertising ecosystem. This role involves building content understanding systems and large-scale infrastructure to deliver near real-time signal updates, facilitating smarter, privacy-aware decision-making across the ad delivery stack. Responsibilities include designing and scaling ML systems for extracting semantic signals from diverse content, contributing to retrieval and ranking pipelines, fine-tuning LLMs for NLP tasks, and constructing knowledge graphs and entity linking systems. The engineer will also work with multimodal data to build robust, cross-domain signal representations.
Good To Have:
- Build core components for content understanding platform
- Own experimentation, offline evaluation, and online validation
- Collaborate across engineering, infra, and product teams
Must Have:
- Design, implement, and scale ML systems for semantic signals
- Contribute to retrieval and ranking pipelines
- Fine-tune LLMs for NLP tasks
- Construct knowledge graphs and entity linking systems
- Work with multimodal data
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! We're seeking a hands-on and experienced Machine Learning Engineer to develop the next generation of ML signal platforms that power retrieval, prediction, and relevance across Apple’s advertising ecosystem. Here you would build 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.
- 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 use 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
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.
- 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
- 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 use 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
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.
- 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
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