Engineering Project Manager - AI Features Internationalization, L&re

Apple Apple · Big Tech · Cupertino, CA · Software and Services

Engineering Project Manager responsible for the internationalization and global launch of AI-driven products, including Apple Intelligence. This role involves leading technical integration of generative AI and ML features across multiple languages and countries, managing international data generation, and driving model evaluation strategy for audio, vision, and language models.

What you'd actually do

  1. AI Feature Orchestration: Facilitate deep technical coordination between Core ML, Software Engineering, Hardware Engineering, and Product Design to integrate AI features into international locales across software and hardware products.
  2. End-to-End Schedule Management: Produce and manage the master schedule for i18n deliverables, ensuring all cross-functional dependencies—from model training and fine-tuning to UI implementation and hardware readiness—are aligned for global launch.
  3. AI Data Operations: Direct the lifecycle of international data generation. Lead timelines for data collection, seek budget approvals for global datasets, coordinate with data collection teams and vendors, and iterate on "data playbooks" to improve model evaluation across diverse languages and regions.
  4. Model Evaluation and Quality: Drive international model evaluation strategy across audio, vision, language, and fusion models. Ensure eval coverage exists for target markets and identify performance gaps that could impact the customer experience internationally.
  5. Hardware-Software AI Integration: Drive international readiness for AI features that span hardware, on-device ML, and companion software—coordinating across hardware engineering, NPS, and regional QA teams for new product introductions (NPI).

Skills

Required

  • 5+ years of experience as an Engineering Program/Project Manager (EPM), Technical Program Manager (TPM), or similar technical leadership role within a software or hardware engineering organization.
  • Proven track record of managing the end-to-end development lifecycle for complex, multi-team features spanning software and hardware.
  • Demonstrated ability to manage complex dependencies across backend engineering (Modeling/Core ML), front-end implementation, hardware, and QA teams across multiple organizations.
  • Direct experience shipping products globally, with a deep understanding of internationalization (i18n) and the architectural and data challenges of scaling AI features for global markets.
  • Ability to drive projects independently, make sound technical decisions with incomplete information, and influence teams without direct authority.
  • Ability to translate highly technical AI/ML concepts into clear, "lightweight" executive-level status updates and risk assessments.

Nice to have

  • Hands-on experience driving AI/ML feature work, including familiarity with Large Language Models (LLMs), vision models, Natural Language Processing (NLP), or model evaluation frameworks.
  • Experience with international launch of hardware products containing ML/AI capabilities, including hardware access restrictions, data collection logistics, and field testing approvals.
  • Experience managing large-scale data generation, annotation, and evaluation workflows specifically for non-English locales and diverse cultural contexts.
  • Technical knowledge of internationalization standards (e.g., Unicode, CLDR) and the architectural challenges of scaling models globally.
  • Experience with demographic representation in ML training data and evaluation, including cultural and religious diversity considerations.
  • Experience managing significant budgets for international data acquisition and coordinating with global data vendors.
  • Ability to use data tools (e.g., SQL, Python, or internal dashboards) to track model performance, project health, and other analytics.

What the JD emphasized

  • managing the technical dependencies, data pipelines, and model evaluation required to ensure Apple's AI features perform with high accuracy, safety, and cultural relevance worldwide
  • end-to-end execution of international features, from initial data collection and model evaluation to final software and hardware integration
  • managing the end-to-end development lifecycle for complex, multi-team features spanning software and hardware
  • Direct experience shipping products globally, with a deep understanding of internationalization (i18n) and the architectural and data challenges of scaling AI features for global markets
  • Hands-on experience driving AI/ML feature work, including familiarity with Large Language Models (LLMs), vision models, Natural Language Processing (NLP), or model evaluation frameworks
  • Experience managing large-scale data generation, annotation, and evaluation workflows specifically for non-English locales and diverse cultural contexts

Other signals

  • driving internationalization of AI features
  • managing technical dependencies for AI models
  • overseeing data collection and model evaluation for global markets