Job description

As a Product Manager on Apple Ads Marketplace, you will work at the intersection of product, machine learning, and large-scale marketplace systems. You 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 deployment. Our current opportunities span two closely related areas: - Ad Matching & 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 systems. - Ad Relevance, Quality & Safety focuses on evaluating and improving the relevance, utility, and safety of ads delivered across Apple's 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 guardrails. You 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. 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 technologies. Hands-on experience with AI/ML systems, with an emphasis on training, fine-tuning, evaluating, and inferencing large-scale deep learning models and LLMs. Strong 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 technologies. Experience with high-throughput, low-latency online inference architectures across client and cloud server environments. Strong technical and analytical foundation, including deep proficiency with SQL and data exploration in large-scale data warehouses. Outstanding written and verbal communication skills, with proven ability to translate complex AI/ML architectures into crisp PRDs, system diagrams, and executive strategy. Demonstrated leadership and cross-functional influence, adept at aligning engineering, applied research, business, and design stakeholders without formal authority. Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Machine Learning, Data Science, or equivalent practical experience. Experience in one or more relevant AI/ML domains, including: Search, information retrieval, matching, recommendations/ranking, query understanding, semantic search, embeddings, vector retrieval, or related systems; or AI/ML evaluation, model quality, relevance, LLM evaluation/raters, classification, model distillation, human-in-the-loop systems, trust & safety, or related systems. Experience building or product-managing large-scale advertising, marketplace, search, recommendation, or content systems. Experience with modern deep learning or LLM systems, including model training, fine-tuning, evaluation, inference, or production deployment. Understanding of high-throughput, low-latency ML systems and the tradeoffs involved in deploying models in production environments. Strong analytical skills, including experience with SQL and large-scale data exploration. Experience designing and analyzing online experiments and using quantitative results to guide product decisions. Experience taking technically complex or ambiguous products from 0→1. Software engineering, data science, ML engineering, research, or other hands-on technical experience is a plus.

Requirements

Must-haves

As a Product Manager on Apple Ads Marketplace, you will work at the intersection of product, machine learning, and large-scale marketplace systems.
Our current opportunities span two closely related areas:
You 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.
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 technologies.
Hands-on experience with AI/ML systems, with an emphasis on training, fine-tuning, evaluating, and inferencing large-scale deep learning models and LLMs.
Experience with high-throughput, low-latency online inference architectures across client and cloud server environments.
Strong technical and analytical foundation, including deep proficiency with SQL and data exploration in large-scale data warehouses.
Outstanding written and verbal communication skills, with proven ability to translate complex AI/ML architectures into crisp PRDs, system diagrams, and executive strategy.
Demonstrated leadership and cross-functional influence, adept at aligning engineering, applied research, business, and design stakeholders without formal authority.
Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Machine Learning, Data Science, or equivalent practical experience.
Experience in one or more relevant AI/ML domains, including:
Search, information retrieval, matching, recommendations/ranking, query understanding, semantic search, embeddings, vector retrieval, or related systems; or
AI/ML evaluation, model quality, relevance, LLM evaluation/raters, classification, model distillation, human-in-the-loop systems, trust & safety, or related systems.
Experience building or product-managing large-scale advertising, marketplace, search, recommendation, or content systems.
Experience with modern deep learning or LLM systems, including model training, fine-tuning, evaluation, inference, or production deployment.
Understanding of high-throughput, low-latency ML systems and the tradeoffs involved in deploying models in production environments.
Strong analytical skills, including experience with SQL and large-scale data exploration.
Experience designing and analyzing online experiments and using quantitative results to guide product decisions.
Experience taking technically complex or ambiguous products from 0→1.

Nice-to-haves

Software engineering, data science, ML engineering, research, or other hands-on technical experience is a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlaillmsql

Platform: apple
Effort: medium