Apple

Apple

Posted via Apple Careers

Machine Learning Engineer - Product Marketing Customer Analytics

Always Hiring

Posted Aug 4, 2026

Role at a glance

Salary
$216.2K – $324.8K/yr
Location
Cupertino, California, United States
Work arrangement
On-site
Employment
Full-time
Experience
8+ years of hands-on programming skills for large-scale data processing
Education
Graduate degree required in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field

Spotted an issue?

We’ll check it against the original posting.

Log in to report

Role Summary

AI-generated

The Product Marketing Customer Analytics team is seeking a Machine Learning Engineer to support Product Marketing, Investor Relations, and the Executive Team with predictive analytics for customer product and services engagement. The role develops scalable machine learning solutions and operationalizes models to provide actionable insights into customer behavior.

What You'll Do

  • Translate product requirements into modeling and engineering tasks
  • Develop scalable machine learning algorithms and models for customer behavior analysis
  • Design and implement end-to-end machine learning pipelines from feature engineering to model serving
  • Develop and optimize deep learning and traditional machine learning solutions on high-volume datasets
  • Manage machine learning projects through data quality, feature development, predictive modeling, visualization, deployment, and maintenance
  • Collaborate with data engineers and infrastructure partners to implement and operationalize robust solutions

Generated from the employer's posting. Verify important details before applying.

View full posting

Qualifications

Graduate degree required in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field; 8+ years of hands-on programming skills for large-scale data processing.

Required

  • 8+ years of hands-on programming skills for large-scale data processing
  • Graduate degree in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field

Preferred

  • Analytical methods and machine learning algorithms including regression, clustering, classification, and optimization
  • 8+ years of experience building and scaling predictive models across distributed systems, production model hosting, and end-to-end...
  • Python and/or Spark for large-scale data processing and machine learning models on structured and unstructured data
  • TensorFlow and PyTorch
  • Feature stores, automated retraining pipelines, and CI/CD integration
  • Oracle, Hadoop, Snowflake, and SQL query optimization
  • Strong communication skills for technical and non-technical stakeholders
  • Cross-functional collaboration and guiding diverse technical teams

Original job description

Content provided by the employer

Summary

At Apple, new ideas have a way of becoming excellent products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish! The Product Marketing Customer Analytics team is seeking a Machine Learning Engineer with deep technical experience in predictive analytics and analytic engineering.

Description

Support Product Marketing, Investor Relations, and the Executive Team with predictive analytics for customer product and services engagement. Understand product requirements then translate them into modeling tasks and engineering tasks
Develop scalable ML algorithms and models to understand customer behavior and provide leadership with actionable insights and recommendations
Design and implement end-to-end machine learning pipelines—from feature engineering to model serving— using best in class MLOps frameworks
Develop and optimize deep learning and traditional ML solutions on high-volume datasets using GPU clusters or distributed CPU environments.
Experiment with cutting-edge algorithms, providing advanced insights into customer behavior and engagement.
Manage ML projects through all phases, including data quality, algorithm/feature development, predictive modeling, visualization, and deployment and maintenance.
Tackle difficult, non-routine analysis/prediction problems, applying advanced ML methods as needed.
Partner with peers to build and prototype analysis pipelines that provide insights at scale.
Collaborate with data engineers and infrastructure partners to implement robust solutions and operationalize models. Enhance and evolve solutions to meet changing business needs with agility.

Minimum Qualifications

8+ years of hands-on programming skills for large-scale data processing
Graduate degree required in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field

Preferred Qualifications

Excellent understanding of analytical methods and machine learning algorithms including regression, clustering, classification, optimization, and other advanced analytic techniques.
8+ years of proven experience building and scaling predictive models across distributed systems (eg: Spark, Kubernetes, GPU clusters), production model hosting, and handling end-to-end performance optimization to solve business problems.
8+ years of hands-on programming skills (Python, and/or Spark) for large-scale data processing, deriving key insights, developing machine learning models on structured and unstructured data, and with demonstrated success maintaining robust, high-throughput ML pipelines in a production environment.
Comfortable with advanced deep learning frameworks (Tensorflow, PyTorch) and adept at designing and scaling ML platforms that include feature stores, automated retraining pipelines and CI/CD integration. Able to design systems to handle high-volume ML workflows and implement scalable, fault-tolerant solutions.
Solid technical database and data modeling knowledge (Oracle, Hadoop, SnowFlake), and experience optimizing SQL queries on large dataset for performance-critical analytics.
Able to work effectively on ambiguous data and constructs within a fast-changing environment, tight deadlines and priority changes
Strong communication skills and ability to explain complex technical topics to both data science peers and non-technical business stakeholders, effectively presenting findings and recommendations to senior executives.
Demonstrated success in partnering cross-functionally, guiding diverse technical teams, aligning business stakeholders, invested in collective success of teams and project outcomes.

Pay & Benefits — Cupertino, California, United States

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 $216,200 and $324,800, 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.

Application Deadline

Apple accepts applications to this posting on an ongoing basis.

Apple

About the company

Apple

Large Enterprise

Apple Inc. is a global technology company known for its innovative products and services, including the iPhone, iPad, Mac computers, and Apple Watch. Founded in 1976, Apple has continuously pushed the boundaries of design and functionality, earning a reputation for high-quality consumer electronics and software solutions like iOS and macOS. With a strong commitment to user experience and privacy, Apple also leads in digital services, offering platforms such as the App Store, Apple Music, and iCloud. The company's focus on sustainability and corporate responsibility further enhances its standing as a leader in the technology sector.