Machine Learning Engineer

Apple News is seeking a Machine Learning Engineer to build, run, and improve infrastructure that powers ML-driven product features at scale. The role emphasizes production model serving, deployment pipelines, distributed systems, and monitoring, alongside work that improves content tagging, ranking, clustering, and personalization.

Responsibilities

• Build and maintain infrastructure for hosting and serving classical ML models such as gradient boosting and SVMs, and deep learning models including transformers and neural rankers, with focus on latency, reliability, and scalability.
• Evaluate tools and frameworks for model serving and feature delivery, including Kubernetes, Spark, Cassandra, Solr, Spring Boot, AWS, and GCP, while developing understanding of trade-offs across latency, cost, scalability, and reliability.
• Partner with model development teams to contribute to a shared codebase, develop common data processing libraries, and help profile and optimize ML workloads.
• Create reusable infrastructure components for training data pipelines, including sampling and data collection, as well as labeling using human annotations or LLMs.
• Design and implement model monitoring, observability, and alerting systems to support production ML reliability and performance SLAs.
• Use real-world user interaction data, with guidance from senior teammates, to identify gaps in training data distributions and to define model success metrics.

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.
• Strong proficiency in Java and/or Python, with interest in production serving systems.
• Experience contributing to or building ML infrastructure components such as model serving, deployment pipelines, or feature delivery systems.
• Some experience deploying ML models on AWS and/or GCP, with an emerging understanding of deployment trade-offs across latency, cost, and scalability.
• 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.
• Good cross-functional communication skills, including the ability to explain technical concepts clearly to teammates.

Technologies

• Java, Python, Kubernetes
• Apache Spark, Cassandra, Solr, Spring Boot
• AWS, GCP
• RAG (retrieval, embedding, chunking, reranking), LLMs
• Gradient boosting, SVMs, transformers, neural rankers
• A/B test analysis

Preferred Qualifications

• Familiarity with inference optimization techniques including 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.
• Experience contributing to AI-powered features with measurable impact on user engagement or content quality.

Pay & Benefits

Base pay range: USD 150,400 - 225,300 per year, depending on skills, qualifications, experience, and location.

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
• Eligible for discretionary restricted stock unit awards
• Ability to purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan
• May be eligible for discretionary bonuses or commission payments and relocation (where applicable)
• Apple benefit, compensation, and employee stock programs are subject to eligibility requirements and the terms of the applicable plan or program