Apple News is building machine learning powered product experiences, and this role helps create the infrastructure that makes those features reliable at scale. You will work on model serving, deployment pipelines, distributed systems, and the monitoring needed to keep ML models performing day to day. In addition, you will support improvements to content tagging, ranking, clustering, and personalization, drawing on real world user interaction data to strengthen training signals.

Responsibilities

Build and maintain production infrastructure to host and serve classical ML models (such as gradient boosting and SVMs) and deep learning models (such as transformers and neural rankers), with a focus on latency, reliability, and scalability
Evaluate and contribute to model serving and feature delivery infrastructure using tools including Kubernetes, Spark, Cassandra, Solr, Spring Boot, AWS, and GCP, learning trade-offs across latency, cost, scalability, and reliability
Collaborate with model development teams on a shared codebase, contribute to common data processing libraries, and profile or optimize ML workloads
Create reusable pipeline components for training data, including sampling and data collection, plus labeling workflows using human annotations or LLMs
Design and implement model monitoring, observability, and alerting systems that support production ML reliability and performance SLAs
Analyze user interaction data (with guidance from senior teammates) to identify gaps in training data distributions and 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 components of ML infrastructure such as 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
Ability to communicate technical concepts clearly across teams

Additional Skills

Familiarity with RAG concepts such as retrieval, embedding, chunking, or reranking (plus)
Experience building or contributing to data pipelines for A/B test analysis or training dataset creation using tools such as Apache Spark

Technologies

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

Benefits

Comprehensive medical and dental coverage
Retirement benefits
A range of 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
Can purchase Apple stock at a discount by voluntarily participating in Apple’s Employee Stock Purchase Plan
May be eligible for discretionary bonuses or commission payments, and relocation (where applicable)

Pay & Benefits

Base pay range for this role is USD 150,400 - 225,300 per year, depending on skills, qualifications, experience, and location. Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Machine Learning Engineer