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About This Role
At Apple, we work every day to create products that enrich people’s 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 Apple’s uncompromising commitment to user privacy and trust.
The 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 Apple’s ecosystem. We are hiring across multiple areas, with current opportunities focused on Ad Matching & Retrieval and Ad Relevance, Quality & Safety.
In 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 systems.
You 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 Apple’s commitment to privacy and user experience.
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.","responsibilities":"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.
Preferred Qualifications
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 01.
Software engineering, data science, ML engineering, research, or other hands-on technical experience is a plus.
Minimum Qualifications
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.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Salary Context
This $150K-$277K range is above the median for AI Product Manager roles in our dataset (median: $194K across 123 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Apple, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 947 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $150K to $277K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Apple AI Hiring
Apple has 28 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, AI Product Manager. Positions span Zurich, Zurich, Switzerland, Cupertino, CA, US, Seattle, WA, US. Compensation range: $194K - $496K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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