Engineering Project Manager - AI Features Internationalization, L&RE

Apple's Software Engineering Operations (SWE Ops) organization is seeking a highly technical Engineering Project Manager (EPM) to drive the internationalization and global launch of AI-driven products, including Apple Intelligence and new hardware with integrated ML capabilities. In this role, you will lead the technical integration of generative AI and machine learning features across 25 languages and 40 countries. You will sit at the critical intersection of Core ML Modeling, Data Science, Hardware Engineering, and Global Product Readiness. You are not just managing localization work—you are 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. You will be responsible for the end-to-end execution of international features, from initial data collection and model evaluation to final software and hardware integration. Minimum Qualifications: 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. Technical Lifecycle Mastery: Proven track record of managing the end-to-end development lifecycle for complex, multi-team features spanning software and hardware. Cross-Functional Leadership: Demonstrated ability to manage complex dependencies across backend engineering (Modeling/Core ML), front-end implementation, hardware, and QA teams across multiple organizations. International Product Expertise: 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. Navigating Ambiguity: Ability to drive projects independently, make sound technical decisions with incomplete information, and influence teams without direct authority. Communication: Ability to translate highly technical AI/ML concepts into clear, "lightweight" executive-level status updates and risk assessments. Preferred Qualifications: AI/ML Domain Depth: 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. New Product Introduction (NPI): Experience with international launch of hardware products containing ML/AI capabilities, including hardware access restrictions, data collection logistics, and field testing approvals. Data Pipeline Management: Experience managing large-scale data generation, annotation, and evaluation workflows specifically for non-English locales and diverse cultural contexts. i18n Engineering Standards: Technical knowledge of internationalization standards (e.g., Unicode, CLDR) and the architectural challenges of scaling models globally. Fairness and Inclusion: Experience with demographic representation in ML training data and evaluation, including cultural and religious diversity considerations. Budget and Resource Strategy: Experience managing significant budgets for international data acquisition and coordinating with global data vendors. Analytical Proficiency: Ability to use data tools (e.g., SQL, Python, or internal dashboards) to track model performance, project health, and other analytics.