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About This Role
Imagine what you could do here. The people here at Apple don’t just create products \- they create the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry\-leading environmental efforts. Join Apple, and help us leave the world better than we found it.
Apple’s Sales organization generates the revenue needed to fuel our ongoing development of products and services. Our World Wide Sales Business Transformation group focuses on implementing strategies, programs, tools, and processes that enable the business to drive sales, improve customer experience, and innovate the way we lead and support one of the largest channel operations in the world. As part of our World Wide Sales Business Transformation group, you will play a critical role in the execution and delivery of AI initiatives that enable the business to drive sales, improve customer experience, and innovate one of the largest channel operations in the world.
We are seeking a highly organized and technically fluent AI Engineering Program Manager to drive the execution of complex AI/ML projects. In this role, you will be the driving force behind our engineering delivery, ensuring that sophisticated AI solutions\-such as conversational agents, RAG systems, and automated workflows\-are built and deployed successfully. You will partner with Product Managers, Data Scientists, AI Engineers, and Security teams to break down complex product roadmaps into actionable engineering milestones, manage cross\-functional dependencies, and clear roadblocks to ensure technical excellence and on\-time delivery.
Description
In this role, you will:
\-Drive the end\-to\-end execution and delivery of key AI and ML platforms supporting the Sales teams \- including but not limited to Security, CSO, Change, and PMO teams.
\-Partner closely with Product Management to translate product strategy and roadmaps into detailed engineering execution plans, sprint schedules, and deliverable milestones.\-
- Manage complex cross\-functional dependencies across Software Engineering, Data Science, UX/UI, Enterprise Architecture, and Information Security teams.
- Proactively identify, track, and mitigate execution risks, technical blockers, and resource constraints before they impact project timelines.
- Facilitate engineering planning, daily stand\-ups, technical design reviews, and sprint retrospectives to ensure team alignment and momentum.
- Track the integration of technical components, such as LLM APIs, data pipelines, and microservices, ensuring all technical moving parts align for seamless deployment.
- Serve as the central point of contact for project status, distilling complex engineering progress into clear, concise executive updates for business partners and leadership.
- Establish and continuously improve engineering delivery processes, fostering a culture of efficiency and collaboration within a fast\-paced, dual\-speed organization.
- Possess the technical depth to understand AI Proof of Concepts (POCs) written in Python, enabling you to engage deeply in technical design discussions and validate scalability with the engineering team.
- Drive the program delivery of MLOps best practices, ensuring engineering teams stay on track to implement version control, CI/CD pipelines, automated testing, and production monitoring for AI systems.
Preferred Qualifications
Background in sales analytics, go\-to\-market strategies, or sales operations.
Direct experience managing the deployment of Machine Learning models or AI applications into production environments.
Understanding of data privacy, enterprise security reviews, and compliance requirements related to AI and data governance.
Familiarity with cloud infrastructure, microservices, and CI/CD deployment processes to better anticipate engineering timelines.
PMP, CSM, or related program management certifications.
Master's degree in Engineering, Computer Science, or a related technical field preferred.
Minimum Qualifications
Typically requires 8\+ years of experience in Engineering Program Management, Technical Project Management, or a related role within software engineering or data organizations.
Proven track record of managing complex, distributed enterprise software or data\-driven products.
Strong technical fluency and the ability to understand architectural diagrams, API integrations, and software development life cycles (SDLC).
Technical proficiency with Python, including the ability to read code, evaluate technical Proof of Concepts (POCs), or run basic scripts to support data\-driven program management and decision making.
Familiarity with artificial intelligence, machine learning concepts, and LLM ecosystems (GenAI, RAG, prompt engineering) to effectively communicate with highly technical teams.
Exceptional organizational skills and expertise in agile methodologies and project management tools (e.g., Jira, Confluence, Wrike, or similar).
Outstanding communication and presentation skills, with the ability to translate technical jargon into business impact for executive stakeholders.
Ability to lead through influence, building strong relationships across matrixed technical and non\-technical teams.
Bachelor’s degree in Computer Science, Engineering, Information Systems, 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 $175,500 and $263,800, 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 $175K-$263K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 Required
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. Disclosed range: $175K to $263K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Apple AI Hiring
Apple has 28 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Safety, AI Product Manager. Positions span Santa Clara, CA, US, Cupertino, CA, US, Seattle, WA, US. Compensation range: $225K - $381K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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