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About This Role
We are seeking an AI Security Architect with deep expertise across security architecture, AI/ML systems, and threat modeling to join the Apple Information Security (AIS) Assurance ARC team. You will own security guidance for the most critical, high\-risk AI initiatives across Apple, shaping architecture patterns, conducting design reviews, selecting or building security solutions for AI\-driven environments, and translating expert decisions into AI\-powered guidance that scales across the entire organization. Your work will directly determine how safely and confidently Apple adopts AI at scale.
Our scope spans across Apple's products, services, and operations, where the adoption of AI is accelerating at an unprecedented pace. The Assurance ARC organization is entrusted with providing expert security architecture guidance for these AI systems, and your contributions will directly impact our ability to protect against evolving AI\-specific threats while enabling the business to innovate securely at scale.
Description
In this high\-impact role, you will bridge the gap between traditional security practices and emerging AI risks. Operating under Zero Trust principles and Secure by Default framework, you will partner with engineering, data science, product, and compliance teams to build "paved roads" \- pre\-approved, reusable, secure\-by\-default patterns that enable teams to build AI\-driven systems without a bespoke review for every project. you will amplify the effectiveness of the entire security function, by reducing inbound request volume through proactive foundational work and providing developers, ML engineers, and traditional security architects with clear guidance and standardized controls,
Leveraging deep AI/ML knowledge, you will evaluate risks such as data poisoning, model manipulation, and prompt injection, while simultaneously applying AI to security workflows to automate threat modeling, design reviews, and control validation. The third dimension of this role beyond prevention and response, is scale: applying AI\-driven tooling to extend the team's reach far beyond what headcount alone can achieve. This is a highly strategic and technical role that bridges AI innovation with security, enabling Apple to adopt AI confidently and at pace.
As part of the Advisory Arm, you will also contribute directly to the Self\-Service Enablement Platform, codifying decisions, patterns, and trade\-offs from your reviews into AI\-powered guidance accessible to teams without direct advisory engagement. Additionally, by working on Apple's most cutting\-edge AI projects, you will serve as an intelligence sensor for the broader ARC team: identifying emerging AI/ML technologies and architectural patterns that should become future foundational focus areas, ensuring the organization stays ahead of the threat landscape.
Preferred Qualifications
Experience performing full\-stack security architecture reviews encompassing cloud\-native and emerging AI/ML technologies
Experience designing security patterns for LLM\-based systems, RAG pipelines, or agentic architectures
Familiarity with Zero Trust architecture principles and Secure by Default design
Experience with data protection, encryption architecture, and key management in AI/ML contexts
Strong verbal and written communication skills, with the ability to build consensus across diverse teams
A real passion for staying ahead of emerging AI security risks and translating that knowledge into actionable guidance
Deeply accountable for your work; upbeat, adaptable, and results\-oriented
Minimum Qualifications
BS in Computer Science, Computer Engineering, or Information Security, or 6\+ years of equivalent, hands\-on security experience in large enterprise environments a plus.
Strong background in security architecture, application security, or cloud security
Demonstrated understanding of AI/ML concepts, pipelines, and risks (e.g., data poisoning, prompt injection, model inversion, model theft)
Experience with threat modeling methodologies (e.g., STRIDE) and secure design principles
Familiarity with AI regulatory frameworks such as the EU AI Act, NIST AI RMF, ISO/IEC 42001, or comparable standards
Ability to communicate security concepts clearly to both technical and non\-technical stakeholders
Proven ability to partner with engineering, data science, and product teams to drive adoption of security requirements
Experience applying AI/LLMs for security automation (e.g., risk analysis, automated threat modeling, compliance validation)
Proficiency in at least one scripting or programming language (e.g., Python, Swift, Java)
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 $172,100 and $258,600, 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 $172K-$258K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 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 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 $185,000 based on 13,200 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($215K) sits 16% above the category median. Disclosed range: $172K to $258K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Apple AI Hiring
Apple has 62 open AI roles right now. They're hiring across AI/ML Engineer, LLM Engineer, AI Product Manager, AI Software Engineer. Positions span Cupertino, CA, US, San Diego, CA, US, Seattle, WA, US. Compensation range: $190K - $487K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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