Interested in this AI/ML Engineer role at Apple?
Apply Now →About This Role
Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. We are seeking a visionary and results\-oriented Head of Product, Global AI Learning Platform to lead a monumental transformation within Apple's Worldwide Channel Strategy \& Operations organization.
This high\-visibility, high\-stakes role is at the forefront of replacing a critical legacy enterprise Learning Management System (LMS) with a cutting\-edge, AI\-powered solution. This next\-generation capability is designed to revolutionize sales training for Apple's field staff (B2C) and global partner network (B2B), directly impacting sales performance, knowledge retention, and the customer experience worldwide.
You will own the end\-to\-end product strategy, roadmap, and execution for this multi\-year, global initiative, while building and leading a high\-performing product and program team in a matrixed environment. This is an unparalleled opportunity to leverage your passion for technology and innovation to deliver measurable business outcomes at Apple's scale. If you thrive on solving complex enterprise challenges, are driven by the opportunity to shape the future of sales learning, and are ready to make a significant impact on Apple's global success, we invite you to lead this strategic imperative.
Description
As the Head of Product, Global AI Learning Platform, you will:
Define \& Champion Product Vision: Develop and continuously evolve a compelling product vision, multi\-year strategy, and roadmap for the new AI\-powered learning solution, strategically balancing innovative AI capabilities with safe, phased legacy system replacement.
Drive Strategic Prioritization: Own critical prioritization decisions across diverse global audiences (B2C retail staff, B2B enterprise \& education partners), ensuring alignment with Apple's strategic business objectives and maximizing impact.
Lead Learning Innovation \& Integration: Champion the integration of cutting\-edge AI/ML capabilities (e.g., personalization, recommendation engines, generative content, NLP/chatbots) to create highly engaging, adaptive, and effective learning experiences.
Oversee Execution Excellence: Lead disciplined product discovery, definition, and delivery processes within an agile framework, ensuring high\-quality requirements, clear epics, and seamless collaboration with internal and external engineering teams.
Manage Complex Migrations: Oversee the intricate migration of legacy content, learner data, and configurations, ensuring data integrity, compliance, and zero disruption during global rollouts and cutovers.
Build \& Empower High\-Performing Teams: Recruit, mentor, and inspire a high\-performing product and program team, fostering a culture of innovation, accountability, continuous improvement, and customer\-centricity in a highly matrixed, global environment.
Strategic Stakeholder Engagement: Serve as the primary product voice, building trust and strong relationships with executive leadership, sales organizations, legal/compliance, geo teams, and external partners to ensure alignment and drive adoption. Closely collaborate with the AIML Technology Development and Innovation team and Sales Engineering.
Risk Management \& Mitigation: Proactively identify and manage program\-level risks (e.g., migration complexity, compliance, partner adoption, AI ethics), developing robust mitigation strategies and clear escalation paths.
Measure \& Optimize Business Impact: Partner with Sales Finance, Advanced Analytics, and regional teams to establish, analyze, and communicate program\-level OKRs and KPIs, directly linked to measurable sales enablement outcomes (e.g., training completion, engagement, knowledge retention, post\-training sales performance uplift, ROI), influencing strategic investment decisions.
Financial Stewardship \& Process Optimization: Lead forecasting, budgeting, and financial analysis for the platform, making data\-driven recommendations for resource allocation and program optimization. Design and optimize global business processes to maximize system adoption and efficiency across Apple's channel ecosystem.
Preferred Qualifications
MBA from a top\-tier program combined with a technical or analytical undergraduate degree (e.g., CS, Engineering, Data Science, Economics, Math).
Master’s degree in AI/ML, Data Science, Educational Technology, or Human\-Computer Interaction (HCI).
Deep domain expertise in learning platforms, EdTech, sales enablement, or workforce training solutions.
Experience managing products with dual\-audience ecosystems (e.g., internal employees and external partners/developers).
Prior experience working with external/offshore engineering teams or vendor partners on large\-scale product development.
Demonstrated ability to thrive in a fast\-paced, high\-growth, and ambiguous environment, continuously simplifying and focusing initiatives.
A strong passion for Apple products, technology, operations, and delivering an extraordinary customer experience.
Exceptional candidates with significant enterprise SaaS product leadership experience (especially in AI features and legacy modernization) who can demonstrate a proven ability to lead complex product organizations and deliver measurable business impact will be considered in lieu of specific degrees.
Minimum Qualifications
10\+ years of progressive product management experience, with at least 5 years in a senior leadership role (e.g., Head of Product, Director of Product, or equivalent) leading enterprise SaaS product initiatives.
Proven track record of successfully defining, launching, and scaling complex, global enterprise products that deliver significant business impact.
Demonstrated experience leading the replacement or significant modernization of a legacy enterprise system (e.g., LMS, HRIS, CRM) with a modern, scalable solution.
Hands\-on experience shipping AI/ML\-powered features (e.g., personalization, recommendation engines, generative AI, NLP) within a product context.
Exceptional strategic thinking, analytical, and problem\-solving skills, with the ability to translate ambiguous challenges into clear product strategies and executable roadmaps.
Proven ability to build, lead, and motivate diverse, high\-performing product and program teams in a matrixed, global environment.
Outstanding written, verbal, and presentation communication skills, with the ability to engage, influence, and build credibility with executive\-level stakeholders and external partners.
Bachelor's degree in Computer Science, Engineering, Business, or a related technical field.
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 $243,100 and $365,400, 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 $243K-$365K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Apple, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($304K) sits 68% above the category median. Disclosed range: $243K to $365K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Apple AI Hiring
Apple has 55 open AI roles right now. They're hiring across AI/ML Engineer, LLM Engineer, Research Scientist, AI Software Engineer. Positions span Austin, TX, US, Cupertino, CA, US, Santa Clara, CA, US. Compensation range: $190K - $487K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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