About Apple
Apple · New York City
ML Data Operations Lead
27.6.2025
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. This includes:
- 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.
Qualifications
- Master’s 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.
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.
Benefits
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