Senior Machine Learning Engineer, Wallet, Payment & Commerce

Summary

Would you like to contribute to Machine Learning and Generative AI technologies? Are you curious about the data that drives AI/ML success? Do you believe Machine Learning and AI can change the world? We truly believe it can!

We are building the data infrastructure that powers machine learning across Wallet, Payment, and Commerce; and synthetic data is at the center of that strategy.

Description

As a Senior Machine Learning Engineer, Wallet, Payment & Commerce specializing in Data Synthesis, you will architect privacy-preserving data generation pipelines that reduce dependency on external data procurement, accelerate model development, and set a new standard for responsible ML at scale.

You'll work at the intersection of cutting-edge generative AI research and production ML systems, collaborating closely with Engineering, Product, Privacy, and Legal teams. This unique opportunity shapes data strategy, impacting features used by millions while pioneering privacy-first ML practices.

Responsibilities

• Design and implement synthetic data generation systems across modalities such as: images, video, time series, and text, while using techniques such as GANs, VAEs, diffusion models, Bayesian/Causal methods, and LLM-based synthesis.
• Innovate on generation techniques to improve realism and representativeness, particularly for edge cases and underrepresented distributions.
• Build and maintain evaluation frameworks to measure synthetic data quality across fidelity, diversity, privacy preservation, and model utility.
• Develop pipelines and tools to automate synthetic data generation for large-scale experiments.
• Mentor and guide junior ML engineers, conducting code reviews and establishing best practices for synthetic data development.

Minimum Qualifications

• BS/Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field, alternatively equivalent industry experience may be considered.
• 5+ years of experience driving the design and development of machine learning pipelines as an ML Engineer.
• Hands-on experience building synthetic data generation systems using modern generative techniques (GANs, VAEs, diffusion models, or LLM-based approaches), with measurable impact on model performance or data cost reduction.
• Hands-on experience synthesizing time series data at scale.
• Proficiency in Python and relevant ML frameworks (PyTorch, TensorFlow).
• Proficiency in Spark, Ray, or other distributed computing technologies for developing pipelines at scale.
• Proficiency in using industry-standard tools and techniques for statistical testing and data experimentation.
• Experience with data augmentation across multiple data types (structured, unstructured, and semi-structured).
• Strong data exploration and analytical skills, with the ability to assess and characterize diverse data assets.
• Proven ability to collaborate across functions (R&D, Privacy, Legal, Infrastructure) and drive cross-team alignment.

Preferred Qualifications

• PhD in Computer Science, Data Science, Statistics, AI/ML, or a related field.
• Experience with Bayesian or causal graph-based approaches to data generation.
• Experience identifying low-quality, erroneous, or fraudulent data at scale.
• Deep familiarity with generative architectures including transformers, diffusion models, and multi-modal systems.
• Track record of influencing cross-team roadmaps and driving adoption of new tools or infrastructure across organizations.

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 $181,100 and $318,400, 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.

Apple accepts applications to this posting on an ongoing basis.

Key skills/competency

• Machine Learning
• Generative AI
• Data Synthesis
• Privacy Preservation
• Distributed Computing
• Time Series Data
• Python
• PyTorch / TensorFlow
• Data Pipelines
• Cross-functional Collaboration