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Sr. Machine Learning Engineer - Apple News
AppleCupertino, California, United StatesSenior$184,700 - $324,8005+ years
Apply Apple News is seeking an experienced Machine Learning Engineer to build, operate, and scale the systems that power intelligent features for millions of people every day. In this role, you will bring deep expertise in model serving, deployment pipelines, distributed systems, and ML platform infrastructure; enabling the team to ship reliable, high-performance ML-powered features across content tagging, ranking, and personalization. You are someone who thrives at the intersection of software engineering and machine learning, and takes pride in building 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 need engineers who are excited to solve them.
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.
- Design, build, and operate infrastructure to host and serve classical ML models (gradient boosting, SVMs) and deep learning models (transformers, neural rankers) in production with a strong focus on latency, reliability, and scalability
- Evaluate and select the right tools, frameworks, and infrastructure (Kubernetes, Spark, Cassandra, Solr, Spring Boot, AWS, GCP) for model serving and feature delivery with a strong command of trade-offs across latency, cost, scalability, and reliability
- Collaborate with model development teams to manage a shared codebase, build common data processing libraries and profile/optimize ML workloads.
- Build scalable and reusable infrastructure components for data pipelines, such as sampling and collecting data for training, labeling via human annotations or LLMs
- Design and implement model monitoring, observability, and alerting systems to ensure production ML systems meet reliability and performance SLAs
- Analyze real-world user interaction data to 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 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
- 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