Machine Learning Engineer, Store Online

About the Role

The Apple Store Online team is responsible for Apple’s largest store, aiming to deliver a magical, personal digital experience where customers can shop, buy, and learn everything Apple, wherever they are. This role is for a passionate, highly motivated, and hands-on applied Machine Learning Engineer to lead the Online Retail Decision Automation team. You will research and develop next-generation algorithms to drive the Apple Online experience, spanning product search, recommendation systems, personalization, Generative AI, and the optimization of Apple-wide systems and infrastructure.

Key Responsibilities

• 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.
• Develop models for product search, recommendation systems (e.g., ranking, page generation), personalization (e.g., evidence, messaging, marketing), and Generative AI.
• Optimize Apple-wide systems & infrastructure.
• Mentor other MLEs and lead an effort to build scalable end-to-end machine learning solutions for retail customers.

Requirements & Tech Stack

• Proficiency in one or more object-oriented programming languages such as Python, Java, C++.
• 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).
• 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 is a plus.
• Experience with Spark, TensorFlow, Keras, and PyTorch is a plus.
• Skilled in communication, problem solving, and strategic thinking.

Qualifications

• Bachelors in a quantitative field, such as Computer Science, Applied Mathematics, Statistics.
• Bachelors degree in a quantitative field with a focus on AI in coursework.
• Understanding of machine learning model lifecycle from prototyping, feature engineering, training, inference, deployment, monitoring, and continuous improvements.