Job Description

Locations

• New York City, New York, United States 10022

Salary

Skills Required

• Machine Learningintermediate
• Generative AIintermediate
• Data Operationsintermediate
• data acquisitionintermediate
• data synthesisintermediate
• data augmentationintermediate
• data scienceintermediate
• annotationintermediate
• data QAintermediate
• end-to-end project managementintermediate
• privacy standardsintermediate
• quality standardsintermediate
• regulatory complianceintermediate
• governanceintermediate
• collaborationintermediate

Required Qualifications

• Master’s degree in Computer Science, Data Science, AI/ML, or related field; or equivalent experience. (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, 5 years)
• Experience with data exploration, data science, and analytical domains, including familiarity with a wide range of unstructured and semi-structured data assets. (experience)
• Familiarity with Machine Learning (ML development lifecycle, typical data workflows, and model metrics) and understanding of how data fits into ML. (experience)
• Excellent problem-solving and program/project management skills. (experience)
• Demonstrated capacity to build solid relationships across organizations and functions (R&D, privacy and legal, tools & infrastructure). (experience)
• Ability to consistently innovate with technical tools, processes and partnerships (internal or external) that improve value across data lifecycle. (experience)
• Scripting skills to automate tasks, compute metrics and explore use of workflows combining ML and human inputs. (experience)

Preferred Qualifications

• Demonstrated ability to handle complex and large scale data ops projects (annotation, collection or QA). (experience)
• Expertise is identifying erroneous, fraudulent or low quality data. (experience)
• Familiarity with pioneering ML techniques, including generative technologies (transformer architecture, computer vision, diffusion models, and multi-modal architectures). (experience)
• Experience in understanding and managing Engineering tools & infrastructure and influencing cross-team roadmaps to align with team/project needs. (experience)
• Demonstrated talent for effecting change and driving results through influence, and an ability to navigate complex organizational structures to foster collaboration across functions. (experience)

Responsibilities

• 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.
• - 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.

Tags & Categories