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Machine Learning Research Scientist - Personalization Science, Apple Fitness+

Apple

Machine Learning Research Scientist - Personalization Science, Apple Fitness+

Apple

Cupertino, CA

·

On-site

·

Full-time

·

1mo ago

Benefits & Perks

Learning and development stipend

Annual team offsites

Wellness benefits

Health, dental, and vision coverage

Parental leave program

Remote work flexibility

Required Skills

Apache Spark

Airflow

PyTorch

About Us

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10000+ employees
Industry: Technology, Information Technology, Software, Consumer Goods & Services

View Company Profile

Wonder how Apple's Media Products show relevant search results and recommendations across App Store, Apple TV, Apple Music, Apple Podcasts, Apple Books, and Apple Fitness+? Join us to conduct research and develop machine learning models that personalize Apple Fitness+ for millions of users worldwide! In this role, you will propose, prototype, and evaluate innovative algorithm solutions and improvements. You will partner to build large-scale personalized recommender systems across Apple's media products and see your work touch the lives of billions of Apple users worldwide.

The Apple Services Engineering team is one of the most exciting examples of Apple's long-held passion for combining art and technology. We are the team that powers recommendations and personalization in the App Store, Apple TV, Apple Music, Apple Podcasts, Apple Books, and Apple Fitness+. We achieve this on a massive scale, meeting Apple's high privacy expectations, and delivering a vast array of fitness and wellness content in over 35 languages to more than 150 countries. Our scientists and engineers build secure, end-to-end solutions powered by machine learning. Thanks to Apple's unique integration of hardware, software and services, designers, scientists, and engineers partner to get behind a single unified vision that always includes a deep commitment to strengthening Apple's privacy policy. Although services are a bigger part of Apple's business than ever before, these teams remain small, flexible, and multi-functional, offering greater exposure to the array of opportunities here.

Description

We are looking for a world-class researcher to help us solve challenging problems in personalization science using the latest advances in artificial intelligence and machine learning. With your expertise, we want to develop novel solutions to power personalized experiences in Apple Fitness+ that enrich our customers' lives by helping them achieve their health and wellness goals. You will have the incredible opportunity to see your solutions deployed at Apple's truly incredible global scale.","responsibilities":"Develop and apply state-of-the-art machine learning and artificial intelligence techniques to personalize user experiences within Apple Fitness+.

Conduct human evaluation and rigorous experiments to test and improve your models.

Derive insights from experimentation and convert them into feature improvements.

Ship production quality code to deploy your models at a global scale.

Contribute to the scientific literature through publications and participation in leading academic conferences.

Help chart the future growth of personalization across Apple's services ecosystem.

Preferred Qualifications

PhD in a quantitative field, including Computer Science, Maths, Statistics, Physics, etc.

Knowledge of generative artificial intelligence applied to recommendation systems.

Minimum Qualifications

Deep knowledge of machine learning-powered personalization algorithms, design patterns, and tools. In particular, this includes deep learning, reinforcement learning, and unsupervised learning methods.

Practical real-world experience with building scalable recommendation systems.

Proven grasp of the open-source Python ML/AI tech stack, including Tensorflow, Py Torch, scikit-learn, numpy, scipy, and pandas.

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Technical competence in production-quality software development.

Familiarity with big data technologies.

Strong written & oral communication skills.MS in a quantitative field, including Computer Science, Maths, Statistics, Physics, etc.

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 .

Client-provided location(s): Cupertino, CA

Job ID: apple-200641734-0836_rxr-660

Employment Type: OTHER

Posted: 2026-01-20T19:10:35
Apply on company site

Perks and Benefits

Health and Wellness

Parental Benefits

Work Flexibility

Office Life and Perks

Vacation and Time Off

Financial and Retirement

Professional Development

Diversity and Inclusion

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About Apple

Apple

Apple

Public

A technology company that designs, manufactures, and markets consumer electronics, personal computers, and software.

10,001+

Employees

Cupertino

Headquarters

$3.5T

Valuation

Reviews

4.0

10 reviews

Work Life Balance

4.0

Compensation

4.2

Culture

3.8

Career

3.5

Management

3.2

75%

Recommend to a Friend

Pros

Great coworkers and people

Excellent benefits and perks

Fast-paced and engaging work environment

Cons

High expectations and pressure

Management quality varies

Limited career progression opportunities

Salary Ranges

17,968 data points

Junior/L3

L2

L3

L4

L5

L6

M3

M4

M5

M6

Principal/L7

Senior/L5

Staff/L6

Junior/L3 · Data Scientist ICT2

0 reports

$121,979

total / year

Base

-

Stock

-

Bonus

-

$103,682

$140,276

Interview Experience

5 interviews

Difficulty

3.4

/ 5

Duration

28-42 weeks

Offer Rate

20%

Experience

Positive 20%

Neutral 40%

Negative 40%

Interview Process

1

Application Review

2

Recruiter Screen

3

Technical Phone Screen

4

Behavioral Interview

5

Onsite/Virtual Interviews

6

Team Matching

7

Offer

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit