Machine Learning Engineer, Apple Store Online

Machine Learning Engineer, Apple Store Online

Cupertino, California, United States Corporate Functions

Imagine what you could do here! The people here at Apple don’t just create products — they build the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry‑leading environmental efforts. At Apple, inclusion is a shared responsibility, and we work together to foster a culture where everyone belongs and is inspired to do their best work.

Here on the Apple Store Online team, we are responsible for Apple’s largest store. Our main goal is to deliver a magical, personal digital experience where customers can shop, buy and learn everything Apple, wherever they are. Each customer should feel like they are our only customer and our job is to set the bar for the experience they receive.

To run such an extraordinary store, it takes extraordinary people, and we are looking for someone to help us do extraordinary things.

We are looking for a passionate, highly motivated, and hands‑on applied Machine Learning Engineer. You will lead the way on our Online Retail Decision Automation team by researching and developing the next generation of algorithms used to drive the Apple Online experience! This role spans central areas of our Apple Online Store including developing models for product search, recommendation systems (e.g. ranking, page generation), personalization (e.g. evidence, messaging, marketing), Generative AI and optimizing Apple‑wide systems & infrastructure.

As a member of the fast‑paced team, you will have the opportunity to be part of new projects and craft upcoming products that will delight and encourage millions of Apple customers every day.

To be successful, you need a strong machine learning background, proven software development skills, a love of learning, and ability to collaborate with cross‑functional teams, including researchers, engineers, data scientists/analysts, and product managers, to develop and implement machine learning algorithms. You’ll mentor other MLEs and lead an effort to build scalable end‑to‑end machine learning solutions for our retail customers.

• Collaborate with other MLEs to build scalable, production‑ready ML solutions, taking algorithms from initial concept through to deployment.
• Contribute to the ongoing improvement of our ML infrastructure and tooling.
• Engage in continuous learning and development, staying up-to-date with the latest advances in machine learning and software engineering.

• Proficiency in one or more object‑oriented programming languages such as Python, Java, C++ and experience building highly scalable distributed systems.
• Hands‑on experience with building data processing pipelines, large‑scale machine learning systems, and big data technologies (e.g. Spark, SQL, Snowflake/Hadoop, etc).
• Bachelor’s in a quantitative field, such as Computer Science, Applied Mathematics, Statistics, or a Bachelor’s degree in quantitative field with a focus on AI coursework.

• Understanding of machine learning model lifecycle from prototyping, feature engineering, training, inference, deployment, monitoring and continuous improvements via deep analysis.
• Experience in Recommender Systems, Personalization, Search, Computational Advertising or Natural Language Processing including RAG‑based Generative AI and transformer architecture.
• Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning in real applications a plus.
• Experience with Spark, Tensor Flow, Keras, and PyTorch a plus.
• Skilled in communication, problem solving, strategic thinking.

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $126,800 and $220,900, and your base pay will depend on your skills, qualifications, experience, and location.

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