Job Description

Locations

• Cupertino, California, United States 95014

Salary

Skills Required

• Machine Learning Engineerintermediate
• developing ML-driven signal platformsintermediate
• building content understanding systemsintermediate
• large-scale infrastructureintermediate
• near real-time signal updatesintermediate
• privacy-aware decision-makingintermediate
• developing rich semantic signalsintermediate
• scalable ad retrievalintermediate
• creative rankingintermediate
• marketplace optimizationintermediate
• LLM fine-tuningintermediate
• knowledge graph constructionintermediate
• semantic searchintermediate
• multimodal representation learningintermediate
• extract structured intelligence from unstructured dataintermediate
• ad tech knowledgeintermediate
• building high-quality privacy-centric signalsintermediate
• ad ranking and relevance systemsintermediate
• signal understandingintermediate
• retrievalintermediate
• multimodal learningintermediate
• LLMsintermediate
• content intelligenceintermediate

Required Qualifications

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

Preferred Qualifications

• 7+ years of experience in machine learning or applied research, with a focus on retrieval, ranking, NLP, or content understanding (experience, 7 years)
• MS or PhD in Computer Science, Machine Learning, Information Retrieval, NLP, or a related field. (degree in phd in computer science)

Responsibilities

• 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

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