Job Details

Weekly Hours: 40

Role Number: 200635900-0157

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 seeking an ML Data Operations Lead to establish and scale Data Curation, Generation and Synthesis across Wallet, Payment, and Commerce. You'll architect privacy-preserving pipelines for accelerated ML development and reduced external data dependencies. 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 operations strategy, impacting features used by millions while pioneering privacy-first ML practices.

Description

As an ML Data Operations Lead, you will architect and drive the strategic vision for machine learning data operations across Wallet, Payment, & Commerce (WPC) creating robust Data pipelines that enable scalable, privacy-preserving ML solutions across multiple product initiatives.

You will lead critical strategic initiatives including:

Data Augmentation & Synthesis Initiative: Pioneer and drive comprehensive data synthesis initiatives to strategically reduce dependency on external data procurement, developing synthetic data generation capabilities that accelerate model development while optimizing costs and enhancing privacy protection.

End-to-End ML Data Excellence: Oversee the complete lifecycle of machine learning data operations—from strategic data acquisition and advanced synthesis/augmentation to data science collaboration, annotation workflows, and rigorous data quality assurance.

This role demands a visionary leader who will elevate data operations from tactical execution to strategic enablement. You will ensure that all data delivered to AI/ML models not only meets Apple's uncompromising privacy and quality standards but actively advances our leadership in privacy-first machine learning, while maintaining full compliance with regulatory and governance requirements.

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, Statistics, or related quantitative field; or equivalent practical experience building ML systems.

• 5+ years of experience in driving the design and development of data infrastructure and machine learning pipelines as an ML Engineer, MLOps Engineer or Data Engineer.

• Hands-on experience designing and deploying synthetic data generation systems using modern techniques (e.g., GANs, VAEs, Diffusion Models, or LLM-based synthesis) with demonstrated impact on model performance or data cost reduction.

• Experience in data augmentation for a variety of data types.

• Experience with data exploration, data science, and analytical domains, including familiarity with a wide range of unstructured and semi-structured data assets.

• Familiarity with Machine Learning (ML development lifecycle, typical data workflows, and model metrics) and understanding of how data fits into ML.

• Excellent problem-solving and program/project management skills.

• Demonstrated capacity to build solid relationships across organizations and functions (R&D, privacy and legal, tools & infrastructure).

• Scripting skills to automate tasks, compute metrics and explore use of workflows combining ML and human inputs.

Preferred Qualifications

• Demonstrated ability to handle complex and large scale data ops projects (annotation, collection or QA).

• Expertise in identifying erroneous, fraudulent or low quality data.

• Familiarity with pioneering ML techniques, including generative technologies (transformer architecture, computer vision, diffusion models, and multi-modal architectures).

• Experience in understanding and managing Engineering tools & infrastructure and influencing cross-team roadmaps to align with team/project needs.

• Demonstrated talent for effecting change and driving results through influence, and an ability to navigate complex organizational structures to foster collaboration across functions.?

• Master’s degree or PhD in Computer Science, Data Science, Statistics, AI/ML, or related field.

• Familiarity with Bayesian/Causal graphs for data generation.

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 (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) .