Middle Product Manager, AI/ML - Apple Ads, Marketplace - Jobs - Careers at Apple
- Define product vision, strategy, roadmaps, and success metrics for large-scale AI/ML systems within Apple Ads Marketplace.
- Partner deeply with ML research, engineering, and data science teams to shape model and system requirements and translate technical capabilities into impactful product experiences.
- Drive products through the full lifecycle, from problem definition and technical exploration through development, experimentation, launch, measurement, and iteration.
- Use data and experimentation to identify marketplace opportunities, diagnose gaps, evaluate model and product performance, and prioritize future investments.
- Define metrics that connect ML system performance with user and advertiser outcomes, including relevance, retrieval quality, coverage, user satisfaction, engagement, and advertiser value.
- Partner across Apple to responsibly incorporate privacy-preserving signals, platform knowledge, and shared technologies into Marketplace systems.
- Communicate complex technical concepts, tradeoffs, and product strategies clearly to engineering, research, business, and executive stakeholders.
- Depending on your area of expertise, drive products across areas such as:
- * Matching & 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.
- * Relevance, Quality & 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 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.
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🇬🇧 English