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Added 12 mins ago
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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

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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: Dec 7, 2026
Posting language: English
Posted: Nov 10, 2026

Original job description

Summary

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

Minimum Qualifications

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

Preferred Qualifications

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

Pay & Benefits

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.

Apply on LinkedIn About employer

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

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

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