Sr. Machine Learning Engineer - Apple News

As a Machine Learning Engineer on the Apple News team, you will build and operate the infrastructure that powers ML-driven product features spanning content tagging, ranking, clustering, and personalization. You will own the systems that host, serve, and monitor both classical and deep learning models in production ensuring reliability, low latency, and scalability at Apple scale. You will evaluate trade-offs across tools and technologies, make sound architectural decisions, and drive ML infrastructure from concept to production. You will collaborate closely with modeling, product, data science, and platform teams to define requirements and deliver features that have measurable impact on user engagement and content quality. Minimum Qualifications MS in Computer Science, Machine Learning, or a related discipline, or equivalent work experience in this domain 5+ years of industry experience in machine learning infrastructure or software engineering with a strong ML systems focus. Strong proficiency in Java and Python for production serving systems Hands-on experience building and shipping production ML infrastructure: model serving, deployment pipelines, and feature delivery systems using AI/ML workflows Experience deploying ML models on cloud platforms (AWS and/or GCP) with a strong understanding of deployment trade-offs across latency, cost, and scalability Experience with RAG architectures: including retrieval, embedding, chunking, and reranking strategies, and deploying agentic AI systems in production Experience building data pipelines for A/B test analysis and training dataset creation using tools such as Apache Spark Strong cross-functional communication skills with the ability to translate complex technical concepts for non-technical partners Preferred Qualifications Familiarity with inference optimization techniques such as quantization, batching, caching, and model distillation to improve serving efficiency Familiarity with embedding pipeline infrastructure: building, storing, refreshing, and serving embeddings at scale; experience with vector store design and trade-offs including indexing strategies, approximate nearest neighbor search, and latency vs. recall considerations Familiarity with content personalization or recommendation systems at consumer scale Track record of delivering AI-powered features with measurable impact on user engagement or content quality