Engineering Project Manager - AI Features Internationalization, L&RE

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. 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. 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. 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. 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. 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). Technical Risk and Mitigation: Proactively identify and mitigate risks unique to global AI, such as linguistic bias, cultural representation gaps in vision models, regional model performance degradation, and data collection constraints in international markets. Stakeholder Leadership: Navigate complex internal organizations to surface risks, drive decisions on feature-by-country gating, and provide clear status to executive stakeholders across engineering, product marketing, and program leadership. 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. 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.

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