Interested in this AI Software Engineer role at Apple?
Apply Now →Skills & Technologies
About This Role
At Apple, the Product Analysis and Compliance Engineering (PACE) organization ensures that every product we ship meets the highest standards of regulatory compliance, product safety, and analytical rigor. We operate at the intersection of engineering, compliance, and data, delivering the insights, testing, and certification workflows that Apple's product programs depend on. Our teams navigate complex regulatory landscapes across dozens of global markets, managing a volume and velocity of compliance work that grows with every product Apple ships.
PACE is building intelligent systems at the intersection of AI, connected data, and compliance, making the organization dramatically more efficient. Our work connects disparate data sources, applies AI to extract insight and automate decision\-making, and puts powerful tools directly in the hands of compliance engineers and analysts. We are seeking a Software Engineer who believes the best way to build great software is to ship early, measure relentlessly, and iterate based on real feedback and real data.
Description
As a Software Engineer on this team, you will design, build, and ship software systems that apply AI to to improve the efficiency of the PACE team. You will work in small iterations, delivering working software early and often, and use data to guide what to build next. You believe that quality is built in \- not bolted on \- and that fast delivery and high standards reinforce each other. You will help establish the engineering culture of a new team: lean practices, continuous delivery, production observability, and a relentless focus on outcomes over output. You are deeply curious \- about about emerging AI capabilities, how users actually work, and how to make tools to enable success \- and you channel that curiosity into building things that matter. You will collaborate closely with PACE domain experts to deeply understand their problems, and with data and AI practitioners to build systems that genuinely work at scale.
","responsibilities":"Design, build, and ship AI\-powered software systems that improve team efficiency, delivering incrementally and iterating based on user feedback
Apply secure engineering practices throughout: secrets management, data classification, access control, and audit logging appropriate for compliance\-sensitive data
Build and maintain robust data pipelines that connect corporate data sources, ensuring data quality, lineage, and accessibility
Effectively use \& improve leading agentic harnesses to build software with your principles, through the development of skills, agents and MCPs
Integrate AI and large language models into production systems with appropriate evaluation, guardrails, and monitoring \- treating models as components, not magic.
Ensure that there is an audit trail for traceability/lineage for AI/LLM based decisions
Establish and maintain continuous delivery pipelines, optimizing for the DORA metrics: deployment frequency, lead time, change failure rate, and mean time to recovery
Build observability into every system from day one \- instrumentation, structured logging, alerting, and dashboards that give the team confidence to ship fast
Write clean, testable, well\-factored code; practice continuous integration, continuous refactoring, and small batch delivery as daily habits
Actively explore the PACE team’s domain, emerging tools, and adjacent problem spaces \- bring new ideas and challenge assumptions
Work directly with PACE team’s domain experts to understand problems deeply before building solutions
Collaborate across teams and organizations to integrate data sources and align on technical direction
Contribute to the engineering culture of a new team \- shaping practices, running retrospectives, and helping the team continuously improve
Represent your work through demos, design discussions, and clear written communication
Preferred Qualifications
Master's degree in Computer Science, Computer Engineering, related field, or equivalent work experience
Experience working in or building software for regulated industries (compliance, legal, safety, or similar domain)
Familiarity with the principles in Accelerate and practical experience improving DORA metrics in a team setting
Experience with test\-driven development, continuous refactoring, small batch delivery, and collective code ownership
Experience securing AI/LLM systems that process sensitive or regulated data, including prompt injection defense, data handling policies, and audit trail requirements
Experience with LLM application patterns: retrieval\-augmented generation, prompt engineering, evaluation frameworks, and human\-in\-the\-loop workflows
Experience with MLOps practices including model versioning, experiment tracking, and performance monitoring in production
Track record of building systems that connect and make sense of heterogeneous data sources at enterprise scale
Experience helping establish engineering culture on a new or transforming team
Minimum Qualifications
Bachelor's Degree in Computer Science, Computer Engineering, related field, or equivalent work experience
3\+ years experience building and shipping production software systems
Strong track record of delivering AI\-powered systems at scale, including model integration, evaluation, and production monitoring
Deep practical experience with modern software engineering practices: continuous integration, continuous delivery, trunk\-based development, and incremental delivery
Proficient in Python and at least one other high\-level programming language
Experience building data pipelines and working with connected data across multiple sources
Experience with cloud infrastructure and container technologies including Kubernetes and Docker
Demonstrated ability to build observability into production systems \- metrics, tracing, logging, and alerting
A curious mindset \- you dig into unfamiliar domains, ask why things work the way they do, and seek out knowledge beyond your immediate responsibilities
Excellent written and verbal communication skills with both technical and non\-technical audiences
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 $220,900, 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-$220K range is below the median for AI Software Engineer roles in our dataset (median: $190K across 251 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 4,133 AI roles we're tracking, AI Software Engineer positions make up 8% of the market. At Apple, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 863 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($184K) sits 21% below the category median. Disclosed range: $147K to $220K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.