Job Description

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. This role 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! The 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 outstanding and great opportunity to be part of new projects and craft upcoming products that will delight and encourage millions of Appleʼs customers every day.

Description

To be successful, candidates will need a strong machine learning background, proven software development skills, a love of learning, and to collaborate with cross-functional teams, including researchers, engineers, data scientists/analysts, and product managers, to develop and implement machine learning algorithms. Mentor other MLE’s and lead an effort to build scalable end-to-end machine learning solutions for our retail customers. RESPONSIBILITIES INCLUDE: - 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.

Minimum Qualifications

• 3+ years of related experience building high throughput scalable applications or building machine learning models.
• Proficiency in one or more object-oriented programming languages such as Python, Java, C++ and experience building distributed systems.
• Experience building data processing pipelines and large scale machine learning systems with experience in big data technologies like Spark, SQL, Snowflake/Hadoop, etc.
• Skilled in communication, problem solving, strategic thinking.

Preferred Qualifications

• Ph.D. or Masters in a quantitative field, such as Computer Science, Applied Mathematics, or Statistics, or equivalent professional experience.
• Experience in Recommender Systems, Personalization, Search, Computational Advertising or Natural Language Processing including RAG based Generative AI and transformer architecture.
• Skilled in communication, problem solving, critical thinking.
• Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning in real applications a plus.
• Experience with Spark, TensorFlow, Keras, and PyTorch a plus.

• Apple is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.

Job Tags

Similar Jobs