Product Manager, AIML Apple Ads, Marketplace

At Apple we work every day to create products that enrich peoples lives. The App Store and Apple Maps are trusted destinations for millions of users to discover apps places products and services. Our advertising platform connects users with high-utility advertiser offerings while maintaining Apples uncompromising commitment to user privacy and Apple Ads Marketplace team is seeking deeply technical Product Managers to help build the next generation of AI/ML systems that power ad discovery relevance quality and safety across Apples ecosystem. We are hiring across multiple areas with current opportunities focused on Ad Matching u0026 Retrieval and Ad Relevance Quality u0026 these roles you will define product strategy and roadmaps for sophisticated machine learning systems operating at massive scale. Depending on your background and area of expertise you may focus on matching user intent to relevant advertiser offerings through modern search and retrieval systems or on evaluating and improving ad relevance quality and safety through advanced AI/ML and LLM-based will partner closely with ML research engineering data science and cross-functional teams to translate advances in AI/ML into products that deliver value for users and advertisers while upholding Apples commitment to privacy and user experience.

As a Product Manager on Apple Ads Marketplace you will work at the intersection of product machine learning and large-scale marketplace will develop a deep understanding of user and advertiser needs identify opportunities within complex ML-driven systems and translate technical capabilities into clear product strategies requirements metrics and execution plans. You will work closely with engineering and applied research teams throughout the ML lifecycle: from data and model development through evaluation experimentation inference and production current opportunities span two closely related areas:nn- Ad Matching u0026 Retrieval focuses on understanding user intent and retrieving high-utility advertiser offerings across the App Store Apple Maps and emerging search and conversational surfaces. Areas of work include semantic and lexical matching query understanding candidate generation embeddings and vector retrieval keyword generation auto-targeting and low-latency retrieval - Ad Relevance Quality u0026 Safety focuses on evaluating and improving the relevance utility and safety of ads delivered across Apples ecosystem. Areas of work include AI/ML evaluation LLM-powered raters relevance and quality scoring model distillation classification human and synthetic feedback systems and real-time quality and safety do not need to bring expertise across both areas. We are interested in technically strong product leaders whose experience aligns deeply with one or more of these problem spaces.

Define product vision strategy roadmaps and success metrics for large-scale AI/ML systems within Apple Ads deeply with ML research engineering and data science teams to shape model and system requirements and translate technical capabilities into impactful product products through the full lifecycle from problem definition and technical exploration through development experimentation launch measurement and data and experimentation to identify marketplace opportunities diagnose gaps evaluate model and product performance and prioritize future metrics that connect ML system performance with user and advertiser outcomes including relevance retrieval quality coverage user satisfaction engagement and advertiser across Apple to responsibly incorporate privacy-preserving signals platform knowledge and shared technologies into Marketplace complex technical concepts tradeoffs and product strategies clearly to engineering research business and executive on your area of expertise drive products across areas such as:n * Matching u0026 Retrieval: semantic and lexical matching query understanding and rewriting candidate generation embeddings and vector search keyword generation auto-targeting retrieval quality and low-latency inference.n * Relevance Quality u0026 Safety: relevance and quality evaluation LLM raters model distillation classification and safety systems human-in-the-loop and synthetic feedback model calibration and real-time quality guardrails.

3 years of technical product management or equivalent experience owning and delivering technically complex products or systems including machine learning AI search advertising or related -on experience with AI/ML systems with an emphasis on training fine-tuning evaluating and inferencing large-scale deep learning models and domain knowledge in one or more relevant AI/ML areas such as search and information retrieval matching and recommendations AI/ML evaluation relevance and model quality LLM systems classification or related with high-throughput low-latency online inference architectures across client and cloud server technical and analytical foundation including deep proficiency with SQL and data exploration in large-scale data written and verbal communication skills with proven ability to translate complex AI/ML architectures into crisp PRDs system diagrams and executive leadership and cross-functional influence adept at aligning engineering applied research business and design stakeholders without formal or Masters degree in Computer Science Electrical Engineering Machine Learning Data Science or equivalent practical experience.

Experience in one or more relevant AI/ML domainsincluding:nSearch information retrieval matching recommendations/ranking query understanding semantic search embeddings vector retrieval or related systems; ornAI/ML evaluation model quality relevance LLM evaluation/raters classification model distillation human-in-the-loop systems trust u0026 safety or related building or product-managing large-scale advertising marketplace search recommendation or content with modern deep learning or LLM systems including model training fine-tuning evaluation inference or production of high-throughput low-latency ML systems and the tradeoffs involved in deploying models in production analytical skills including experience with SQL and large-scale data designing and analyzing online experiments and using quantitative results to guide product taking technically complex or ambiguous products from engineering data science ML engineering research or other hands-on technical experience is a plus.