Machine Learning Engineer, Apple Store Online
- Cambridge, ON, Canada
The base pay range for this role is between $116,400 and $203,000, and your base pay will depend on your skills, qualifications, experience, and location. · Full-time
Applications open in a new tab.
Job details
- Pay
- The base pay range for this role is between $116,400 and $203,000, and your base pay will depend on your skills, qualifications, experience, and location.
- Job type
- Full-time
- Apply by
- Posting language
- English
- Posted
Original job description
Posted: May 28, 2026
Role Number:200665562-1242
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 outstanding and great opportunity to be part of a new projects and craft upcoming products that will delight and encourage millions of Appleʼs customers every day.
Description
To be successful, you 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. You’ll mentor other MLE’s and lead an effort to build scalable end-to-end machine learning solutions for our retail customers
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
- 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 (eg: Spark, SQL, Snowflake/Hadoop, etc)
- Bachelors in a quantitative field, such as Computer Science, Applied Mathematics, Statistics, or Bachelors degree in 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 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, TensorFlow, 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 $116,400 and $203,000, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote 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
At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here - in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.
Learn about accessibility in Apple’s workplace
Learn about reasonable accommodations for job applicants
Apple accepts applications to this posting on an ongoing basis.
More about this job
Responsibilities
Develop and deploy scalable, production-ready machine learning solutions for Apple’s online retail experience, including search, recommendations, personalization, and generative AI. Improve machine learning infrastructure and tooling, collaborate across teams, and mentor other machine learning engineers.
Requirements
Requires a bachelor’s degree in a quantitative field and proficiency in an object-oriented programming language, along with experience building scalable distributed systems, data pipelines, and large-scale machine learning systems. Relevant experience may include recommender systems, personalization, search, natural language processing, generative AI, deep learning, or reinforcement learning; strong communication, problem-solving, and strategic thinking are also valued.
Skills
- Machine Learning
- Python
- Java
- C++
- Distributed Systems
- Data Processing Pipelines
- Big Data
- Spark
- SQL
- Snowflake
- Hadoop
- Recommender Systems
- Personalization
- Search
- Generative AI
- TensorFlow
Benefits
- Medical Coverage
- Dental Coverage
- Retirement Benefits
- Discounted Products
- Free Services
- Education Expense Reimbursement
- Restricted Stock Units
- Employee Stock Purchase Plan
- Discretionary Bonuses
- Relocation Assistance
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
- Retail
Keywords
- Machine Learning
- Applied Machine Learning
- Python
- Java
- C++
- Distributed Systems
- Data Processing Pipelines
- Big Data
- Spark
- SQL
- Snowflake
- Hadoop
- Machine Learning Lifecycle
- Feature Engineering
- Model Training
- Model Inference
- Model Deployment
- Model Monitoring
- Recommender Systems
- Personalization
- Product Search
- Ranking
- Natural Language Processing
- Retrieval-Augmented Generation
- Generative AI
- Transformer Architecture
- Deep Learning
- Bandits
- Probabilistic Graphical Models
- Reinforcement Learning