Machine Learning Engineer - Apple News

Apple • Cupertino, CA

3d

About The Position

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 day. In 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 we're 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.

Requirements

• MS in Computer Science, Machine Learning, or a related discipline, or equivalent work experience in this domain
• 2+ years of industry experience in machine learning infrastructure or software engineering with exposure to ML systems
• Solid proficiency in Java and/or Python, with an interest in production serving systems
• Experience contributing to or building components of ML infrastructure: model serving, deployment pipelines, or feature delivery systems
• Some exposure to deploying ML models on cloud platforms (AWS and/or GCP), with a developing understanding of deployment trade-offs across latency, cost, and scalability
• Good cross-functional communication skills, with the ability to explain technical concepts clearly to teammates

Nice To Haves

• Familiarity with RAG concepts (retrieval, embedding, chunking, or reranking strategies) is a plus
• Experience building or contributing to data pipelines for A/B test analysis or training dataset creation using tools such as Apache Spark
• Familiarity with inference optimization techniques such as quantization, batching, caching, and model distillation to improve serving efficiency
• Exposure to embedding pipeline infrastructure or vector store concepts, such as indexing strategies, approximate nearest neighbor search, and latency vs. recall considerations
• Interest in content personalization or recommendation systems at consumer scale
• Any experience contributing to AI-powered features with measurable impact on user engagement or content quality

Responsibilities

• 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 scalability
• Contribute 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, andReliability
• Collaborate with model development teams to contribute to a shared codebase, build common data processing libraries, and help profile/optimize ML workloads
• Build reusable infrastructure components for data pipelines, such as sampling and collecting data for training, and labeling via human annotations or LLMs
• Help design and implement model monitoring, observability, and alerting systems to support production ML systems in meeting reliability and performance SLAs
• Analyze real-real user interaction data, with guidance from senior teammates, to help uncover gaps in training data distributions and derive model success metrics

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level