ML Data Operations Lead, Wallet, Payment & Commerce

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 looking for a talented individual to drive Data Operations supporting ML features, in close collaboration with our AI, Engineering, Product, and Legal partners, and to run the corresponding data projects end-to-end. We invite you to join us at this exciting time! Grow fast and positively impact multiple critical features on your first day at Apple!

Description

As an ML Data Ops Lead, you will focus on data acquisition, data synthesis/augmentation, data science, annotation, and data QA. This role is responsible for overseeing the end-to-end process for the machine learning data needs of AI/ML partners within Wallet, Payment, & Commerce (WPC). From conceptualization to completion, you will ensure that the data delivered to AI/ML models meets Apple's rigorous privacy and quality standards and meets regulatory/governance requirements.","responsibilities":"Own project planning and coordination for large Data Engineering initiatives, including requirements gathering, scoping effort, prioritizing, resource allocation, and scheduling of deliverables.

Design and implement ML Data Ops strategies optimized for each feature (collection and annotation), including the identification and sourcing or creation of necessary tooling or infrastructure.

Drive data governance and other regulatory/privacy initiatives and make sure that processes are well documented and maintained to the standards of Apple.

Collaborate with vendors to ensure tasks are calibrated appropriately; track and report on quantity and quality metrics.

In collaboration with our Engineering Program Manager, establish robust processes to facilitate the expression of needs, as well as the efficient planning, tracking and reporting of data programs.

Drive or promote enhancements of data operations across features supported (increase diversity & quality, reduce cost & lead time), through innovative workflows that combine human and machine computation (using new capabilities of ML & foundation models).

Partner with our Engineering Managers to help execute on the long term engineering initiatives by building a roadmap that balances short term requests and long term initiatives.

Preferred Qualifications

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

Expertise is 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.

Minimum Qualifications

BS/Masters degree in Computer Science, Data Science, AI/ML, or related field; or equivalent experience.

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

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).

Ability to consistently innovate with technical tools, processes and partnerships (internal or external) that improve value across data lifecycle.

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

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 .