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About This Role
Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.
The AI and Data Platform team is building Gen AI capabilities which include knowledge discovery, conversational capabilities, workflow automation, developer productivity, and more. Our mission is to increase the productivity and efficiency across Apple's Business Units. As these capabilities mature, the problems we are solving are shifting from "can AI do this?" to "how do we make AI trustworthy, governable, and reliable at enterprise scale?"
We are looking for a Product Manager to join this team and take on some of the most consequential product challenges in this space. This role is ideal for someone who is deeply technical, thinks in systems and architectures rather than just features, builds prototypes to learn fast, and thrives in environments where the product category itself is still being defined. You will work at the intersection of AI/ML, enterprise infrastructure, security, and developer experience \- and you will have a direct impact on how Apple operates with AI across its business.
Description
As a Product Manager on the GenAI team, you will define, build, launch, and iterate on products and capabilities that shape how AI is deployed and operated across Apple. You will partner closely with engineering, security, data science, and business teams to solve hard problems that span technical architecture, governance, and organizational adoption. The ideal candidate has a proven product management track record in AI/ML or infrastructure products, strong technical depth, and the ability to think creatively about new and emerging product categories. You are expected to navigate across organizational boundaries, influence decision\-making at all levels, and manage a portfolio of products in a rapidly evolving space.","responsibilities":"Define and execute product strategy, roadmap, and delivery for GenAI capabilities that serve Apple's enterprise teams
Make product decisions that are deeply intertwined with technical architecture \- you will engage on system design, protocol choices, security models, and infrastructure trade\-offs alongside senior engineers
Partner with teams across Apple to understand their AI needs, articulate user journeys and use cases, identify pain points, and translate those into product requirements
Build prototypes and proof\-of\-concepts using AI coding tools such as Claude Code, Cursor, or similar \- we expect PMs to "vibe code", validate ideas hands\-on, and stay close to the technology rather than only writing specs
Drive adoption of products and capabilities across multiple internal teams, often through influence rather than authority
Track the competitive landscape across GenAI products, agentic systems, orchestration frameworks, and emerging protocols to inform product direction
Research best\-in\-class industry approaches and conduct technical teardowns of competitor offerings to inform Apple's product direction
Define and measure success through metrics frameworks \- you can design KPIs for a product that didn't exist six months ago
Understand technical problems and dependencies, partner with cross\-functional teams to ensure effective product launches and deliver a world\-class product experience
Translate complex technical concepts into clear product specifications, user stories, and prioritized backlogs
Discuss complex industry\-specific business and technical concepts with executives, managers, and technologists
Communicate product vision, strategy, and progress to audiences ranging from individual engineers to senior executives
Preferred Qualifications
5\+ years of product management experience
Advanced Degree Technical degree (i.e., Master in Computer Science, Engineering, Data Science) or equivalent practical experience
Experience with agentic AI systems, agent frameworks, orchestration engines, or multi\-agent architectures
Familiarity with emerging AI infrastructure protocols and standards for tool integration, agent interoperability, and workload identity
Experience building products where governance, compliance, audit trails, and access control were core to the product rather than afterthoughts
Background in cloud\-native architectures, distributed systems, or container orchestration
Python and SQL working proficiency
Experience driving platform or product adoption across large organizations with multiple independent teams
Experience evaluating build\-vs\-buy decisions and technology partnerships
Understanding of enterprise security patterns including delegated authorization, policy enforcement, and runtime isolation
Familiarity with the GenAI competitive landscape including offerings from major cloud providers and key startups
Minimum Qualifications
2\+ years working on platform, infrastructure, developer tools, or enterprise systems products
Experience shipping AI/ML\-powered products in production environments, not just prototypes or demos
Strong technical background with the ability to engage meaningfully with senior engineers on system architecture, API design, and security trade\-offs
Hands\-on proficiency with AI coding tools (Claude Code, Cursor, Replit, or equivalent) for rapid prototyping and idea validation
Experience working cross\-functionally with engineering, security, design, and business stakeholders
Strong analytical skills with a data\-driven approach to prioritization and decision\-making
Excellent written and verbal communication skills with the ability to influence without direct authority
Bachelor’s Science Computer Science, Engineering, Data Science, or similar, or equivalent practical experience
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .
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 $147,400 and $272,100, 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 $147K-$272K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $174K across 475 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 26,159 AI roles we're tracking, AI Product Manager positions make up 2% 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 $204,600 based on 532 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. Disclosed range: $147K to $272K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Apple AI Hiring
Apple has 160 open AI roles right now. They're hiring across Research Engineer, MLOps Engineer, AI/ML Engineer, AI Software Engineer. Positions span Cupertino, CA, US, Austin, TX, US, Santa Clara, CA, US. Compensation range: $153K - $487K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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