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Apple’s Security Engineering \& Architecture organization protects the systems and experiences used by people around the world. The ML Security Engineering team brings together machine learning, systems security, and product engineering to help ensure that Apple Intelligence and other AI\-powered experiences are secure, private, and trustworthy.
We are seeking an AI Systems Security Engineer to design, build, and deploy the security foundations for agentic and tool\-using AI systems. You will develop the architectural controls that govern how agents access tools, services, memory, user data, and other privileged capabilities\-and ensure those controls remain effective when models encounter malicious, untrusted, or adversarial inputs.
You will work closely with ML security researchers, AI/ML platform teams, operating\-system engineers, product security, privacy, and product teams. Research will identify emerging attacks and promising defenses; you will translate those findings into robust platform capabilities, reusable frameworks, launch requirements, and production protections. Success in this role is measured by security properties that can be enforced, tested, observed, and maintained across shipping systems.
Description
Agentic AI systems introduce a new security boundary: probabilistic models interpret untrusted content while making decisions that can affect tools, data, and user\-visible state. Securing these systems requires more than model\-level safeguards. It requires carefully designed trust boundaries, least\-privilege interfaces, execution controls, isolation mechanisms, and defense\-in\-depth across the complete AI system.
In this role, you will own critical elements of that security architecture. You will build systems that mediate agent actions, constrain authority, preserve data and instruction provenance, isolate untrusted execution, and prevent compromised model behavior from becoming unauthorized system behavior.
You will also develop the infrastructure needed to validate these protections continuously: adversarial integration tests, security invariants, policy conformance checks, telemetry, deployment gates, and tools for investigating failures. The solutions must meet demanding product requirements for latency, reliability, privacy, debuggability, and compatibility across Apple platforms and services.","responsibilities":"Design security architectures for AI agents that interact with tools, APIs, applications, memory, external content, and sensitive user data.
Build runtime controls for capability authorization, action mediation, least\-privilege access, context isolation, data\-flow enforcement, and secure tool execution.
Establish clear trust boundaries between models, orchestration components, tools, third\-party content, local applications, cloud services, and user data.
Translate ML security research\-including findings related to indirect prompt injection, goal hijacking, tool misuse, privilege escalation, persistence, confused\-deputy behavior, and cross\-context data leakage\-into production\-ready defenses.
Develop reusable security frameworks and platform primitives that product teams can adopt without implementing bespoke controls for each AI experience.
Define and enforce security invariants that remain valid even when model outputs are incorrect, adversarially influenced, or otherwise untrustworthy.
Build adversarial testing and validation infrastructure for multi\-step agent workflows, including continuous evaluation, regression testing, policy verification, and security\-focused launch gates.
Harden the agent ecosystem, including tool registration, capability discovery, memory systems, workflow state, model context construction, external integrations, and software supply\-chain dependencies.
Develop privacy\-preserving telemetry and diagnostic mechanisms for detecting policy violations, investigating security failures, and measuring the effectiveness of deployed defenses.
Lead threat modeling and architecture reviews for new agent capabilities, translating identified risks into concrete engineering requirements and release criteria.
Partner with ML security researchers to productionize promising mitigations and provide system\-level feedback that informs future research.
Work across product, platform, privacy, and security teams to drive security improvements from initial architecture through deployment and long\-term maintenance.
Provide technical leadership, establish engineering standards, mentor engineers, and influence the security architecture of agent systems across Apple.
Preferred Qualifications
Experience securing LLM\-based, agentic, tool\-using, or other probabilistic AI systems.
Experience with capability systems, sandboxing, policy engines, information\-flow controls, provenance systems, secure IPC/RPC, or workload isolation.
Familiarity with attacks against agent systems, including prompt injection, unauthorized tool use, privilege escalation, data exfiltration, memory poisoning, and multi\-stage attacks.
Experience building security evaluation infrastructure, fuzzing systems, adversarial test frameworks, runtime monitors, or automated release gates.
Experience securing operating systems, distributed systems, application platforms, cloud services, or privacy\-sensitive consumer products.
Understanding of ML inference systems and the interaction between models, context construction, orchestration layers, retrieval systems, tools, and product code.
Ability to evaluate security designs against practical constraints such as latency, availability, privacy, compatibility, and diagnosability.
Track record of delivering foundational security mechanisms adopted by multiple products or engineering organizations.
Excellent written and verbal communication skills, including the ability to explain subtle security properties to research, engineering, and product audiences.
Minimum Qualifications
Bachelor’s degree in computer science, computer engineering, security, or a related field, or equivalent practical experience.
Significant experience designing and shipping security\-critical systems, platform security mechanisms, or large\-scale systems software.
Strong understanding of security architecture, trust boundaries, least privilege, authorization, isolation, secure execution, and defense\-in\-depth.
Demonstrated ability to convert threat models and security requirements into reliable production implementations.
Strong software engineering skills in one or more systems or platform languages, with experience building maintainable, testable, and performance\-sensitive software.
Experience working across organizational boundaries to influence architecture and deliver security improvements in complex production systems.
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 $184,700 and $324,800, 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 $184K-$324K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($254K) sits 19% above the category median. Disclosed range: $184K to $324K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Apple AI Hiring
Apple has 57 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Research Scientist, AI Product Manager. Positions span Cupertino, CA, US, Sunnyvale, CA, US, San Diego, CA, US. Compensation range: $214K - $401K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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