About the role

What will you do at Apple?

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.
DESCRIPTION
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.
MINIMUM QUALIFICATIONS
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
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, with the ability to explain
technical concepts clearly to teammates
PREFERRED QUALIFICATIONS
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

Which skills does this role require?

Machine LearningJavaPythonModel ServingDeployment PipelinesDistributed SystemsML InfrastructureAWSGCPRAGVector StoresSoftware EngineeringContent TaggingRankingClusteringScalabilityA/B TestingDeep LearningClassical ModelsFeature DeliveryPrivacy-preservingOn-device

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