Job Description

Apple's Ads Signals Intelligence team is seeking a hands-on and experienced Machine Learning Engineer to develop ML-driven signal platforms that power retrieval, prediction, and relevance across Apple's advertising ecosystem. This role focuses on building content understanding systems and large-scale infrastructure for real-time signal updates, enhancing decision-making in ad delivery.

Requirements

4+ years of experience in machine learning or applied research, focusing 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 ML frameworks like PyTorch or 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.

Responsibilities

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 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