Job Details

Weekly Hours: 40

Role Number: 200602019-0836

Summary

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.

Description

• 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

Minimum Qualifications

• 3+ years of experience in ML 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 degree, or equivalent experience, in Computer Science, Machine Learning, Information Retrieval, NLP, or a related field or experience

Preferred Qualifications

• 5+ years of experience in ML or applied research, with a focus on retrieval, ranking, NLP, or content understanding

• Exposure to ad tech domains such as auction modeling, targeting, attribution, or creative optimization

• MS or PhD, or equivalent experience, in Computer Science, Machine Learning, Information Retrieval, NLP, or a related field

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) .