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Engineering Program Manager, Data - Special Projects

Summary

Leads data-driven AI initiatives at Apple, managing complex projects from ideation to execution, bridging technical teams to deliver high-quality datasets and infrastructure for next-gen AI/ML experiences.

Apple's Special Projects team is seeking an Engineering Program Manager (EPM) to drive critical data initiatives that power our next-generation AI and machine learning experiences. In this role, you'll manage complex data programs from ideation through execution, partnering with cross-functional teams to deliver high-quality datasets and infrastructure that enable breakthrough AI capabilities.

You will be the voice of your projects, building defensible plans, securing stakeholder buy-in, and ensuring successful delivery. This role requires strong program management fundamentals, technical acumen in data systems, and the ability to independently drive clarity from complexity while maintaining program momentum. You'll work at the intersection of design, research, engineering, and operations to develop and transform data requirements into actionable programs that meet Apple's quality standards.

Lead high-complexity projects from ideation through definition, execution, and closure for data initiatives. Translate technical requirements into well-defined, actionable project plans and deliverables. Independently identify risks, proactively develop mitigation strategies, and drive conflict resolution. Distill and communicate project status, risks, and recommendations to stakeholders at all levels.

Minimum Qualifications

  • 5+ years of program/project experience managing complex, cross-functional technical programs.
  • Proven track record of independently identifying and mitigating risks in ambiguous, fast-paced environments.
  • Exceptional communication and presentation skills.

Preferred Qualifications

  • Bachelor's degree in Computer Science, Engineering, Data Science, or related field.
  • Hands-on experience with data-centric projects, from collection and annotation to model training and evaluation.
  • Experience driving process improvements in data-centric programs.

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