Machine Learning Engineer Apple News

Apple News is seeking a Machine Learning Engineer to help build operate and grow the systems that power intelligent features for millions of people every this role you will build hands-on experience with model serving deployment pipelines distributed systems and ML platform infrastructure working alongside senior engineers to ship reliable high-performance ML-powered features across content tagging ranking and personalization. You are someone who is excited to grow at the intersection of software engineering and machine learning and takes pride in contributing to the infrastructure that makes great models matter at scale. At Apple News our ML problems are uniquely hard spanning privacy-preserving personalization on-device considerations and the balance between editorial and algorithmic curation and were looking for engineers who are eager to learn and grow while helping solve them.

As a Machine Learning Engineer on the Apple News team you will contribute to building and operating the infrastructure that powers ML-driven product features spanning content tagging ranking clustering and personalization. With guidance from senior engineers you will help build and maintain systems that host serve and monitor both classical and deep learning models in production with a focus on reliability low latency and scalability at Apple scale. You will develop your understanding of trade-offs across tools and technologies contribute to architectural discussions and help drive well-scoped pieces of ML infrastructure from concept to production. You will collaborate closely with modeling product data science and platform teams to help define requirements and deliver features that have measurable impact on user engagement and content quality.

Build and help maintain infrastructure to host and serve classical ML models (gradient boosting SVMs) and deep learning models (transformers neural rankers) in production with a focus on latency reliability and scalabilitynContribute to the evaluation of tools frameworks and infrastructure (Kubernetes Spark Cassandra Solr Spring Boot AWS GCP) for model serving and feature delivery developing a growing understanding of trade-offs across latency cost scalability and reliabilitynCollaborate with model development teams to contribute to a shared codebase build common data processing libraries and help profile/optimize ML workloadsnBuild reusable infrastructure components for data pipelines such as sampling and collecting data for training and labeling via human annotations or LLMsnHelp design and implement model monitoring observability and alerting systems to support production ML systems in meeting reliability and performance SLAsnAnalyze real-world user interaction data with guidance from senior teammates to help uncover gaps in training data distributions and derive model success metrics

MS in Computer Science Machine Learning or a related discipline or equivalent work experience in this domainn2 years of industry experience in machine learning infrastructure or software engineering with exposure to ML systemsnSolid proficiency in Java and/or Python with an interest in production serving systemsnExperience contributing to or building components of ML infrastructure: model serving deployment pipelines or feature delivery systemsnSome exposure to deploying ML models on cloud platforms (AWS and/or GCP) with a developing understanding of deployment trade-offs across latency cost and scalabilitynFamiliarity with RAG concepts (retrieval embedding chunking or reranking strategies) is a plusnExperience building or contributing to data pipelines for A/B test analysis or training dataset creation using tools such as Apache SparknGood cross-functional communication skills with the ability to explain technical concepts clearly to teammates

Familiarity with inference optimization techniques such as quantization batching caching and model distillation to improve serving efficiencynExposure to embedding pipeline infrastructure or vector store concepts such as indexing strategies approximate nearest neighbor search and latency vs. recall considerationsnInterest in content personalization or recommendation systems at consumer scalenAny experience contributing to AI-powered features with measurable impact on user engagement or content quality