Interested in this AI/ML Engineer role at Apple?
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About This Role
Would you like to drive the future of Apple's data platform and shape how AI fundamentally transforms the way we build, operate, and scale data at Apple, while having the unique opportunity to impact some of the most far\-reaching software applications in the world?
Description
The iCloud Data organization within Apple Services enables iCloud users to access all their content across apps (Photos, Mail, Messages, FaceTime, Calendar, Enterprise \& Education etc) on every device, all the time, through consistent, scalable, timely, accurate, complete and fully integrated data infrastructure that surfaces relevant information. We are investing deeply in a new generation of AI\-native capabilities, agents, intelligent workflows, and self\-serve analytics, to accelerate our Data Engineering and Data Science teams and define what an AI\-first data organization looks like at Apple scale. If this excites you and you're energized by taking novel AI techniques from research to production on hard, high\-leverage, high\-scale problems, we'd love to hear from you! We're seeking a top\-tier Applied AI Engineer with strong architectural thinking, deep AI/ML knowledge and robust software skills, who has built AI products end\-to\-end, has sharp intuition for LLMs, agents, retrieval and evaluation, and shares our passion for trustworthy data\-driven products at Apple.","responsibilities":"Build the AI foundation of our data platform, scalable and trustworthy AI products, agents and workflows that power self\-serve analytics, experimentation, and data engineering across iCloud, in partnership with Engineering, Data Science, Product, Platform and Research, improving how we build, operate, and scale data for billions of users worldwide.
Design, build and own AI systems end\-to\-end, from retrieval, planning and reasoning, through evaluation, guardrails and observability, to deployment and the on\-call rotation that keeps them trustworthy.
Drive cost, performance and inference\-quality efficiency across our AI systems, making thoughtful model selection and serving decisions, optimizing latency, throughput and token economics, and introducing techniques (caching, batching, distillation, quantization, speculative decoding) that let us scale AI capabilities sustainably at Apple scale.
Build deep domain expertise across our data and AI stack, product and business, and be an advocate for engineering excellence and responsible AI.
Explore and introduce state\-of\-the\-art AI techniques, models, agentic patterns, evaluation methods, and AI\-native developer tools, translating them into capabilities like natural\-language data interfaces, AI\-accelerated pipeline development, and intelligent alerting that make Data Engineering and Data Science teams materially faster.
Educate and uplevel the broader Data organization on modern AI patterns, running workshops, authoring technical playbooks and design guidance, mentoring engineers and scientists, and helping the team adopt AI\-native practices that accelerate both the engineering and data science lifecycle.
Preferred Qualifications
Model and prompt customization at scale: fine\-tuning foundation models, training reward models, building custom retrieval, reranking or embedding models for domain\-specific tasks, and prompt engineering with performance, reliability and safety optimization.
Experience with MLOps and LLMOps, model lifecycle management, deployment pipelines, observability, and prompt and evaluation versioning.
Experience building natural\-language interfaces over data, text\-to\-SQL, semantic search, or analytics copilots, for both internal and customer\-facing use cases.
Experience leveraging AI\-native code editors and agent\-assisted development environments to improve developer productivity, and establishing guardrails for their responsible use (security, IP protection, compliance, code quality).
Experience with cloud computing platforms (AWS, Google Cloud, Azure) and stream\-processing systems (Apache Flink, Spark\-Streaming, Kafka Streams) for real\-time data and real\-time AI applications.
Experience building AI solutions for machine learning, experimentation and responsible AI in regulated or privacy\-sensitive environments. Contributions to open source, research, talks or technical writing that has shaped how others build AI systems.
Minimum Qualifications
8\+ years of software engineering experience building scalable systems, reusable tools and frameworks, with 3\+ years taking LLM or agentic systems from prototype to production, and deep fluency in the modern AI stack.
You architect, build and operate production\-grade AI products composed of LLMs, foundation models, agents and deterministic components, for both human and machine consumption, with clear judgment on inference\-versus\-compute boundaries, task decomposition across specialized models, orchestration of multi\-step reasoning and tool use, and graceful degradation under failure.
Solid foundation in machine learning and deep learning. You understand how modern models (transformers, LLMs) are trained, fine\-tuned and evaluated, reason about embeddings, loss functions and statistical rigor, and can diagnose whether a production issue is prompt, retrieval, model or data.
Proficiency in at least one high\-level language (Python, Scala, Java, or Go), and the discipline to write code that is readable, observable in production, and testable at the boundaries.
Hands\-on fluency with modern LLM and agent frameworks (LangChain, LlamaIndex, Semantic Kernel, Google ADK or equivalent), vector databases (FAISS, Chroma or similar), and agentic architectures, multi\-agent coordination, tool invocation and stateful reasoning. You've moved beyond vanilla RAG and embeddings, knowing where they help, where they break, and when to reach for planning, reranking, structured reasoning, fine\-tuning or deterministic compute instead.
Production discipline for AI systems: evaluation harnesses, guardrails and telemetry that change decisions (offline evals, golden sets, LLM\-as\-judge, behavioral regression, drift monitoring); and optimization for cost, latency, throughput and inference quality (model selection, serving decisions, token\-spend control, caching, batching, streaming, distillation, quantization, speculative decoding).
Experience with the data infrastructure ecosystem, SQL engines (such as Trino, Presto or Spark), lakehouse architectures, workflow orchestration, and streaming systems, and the ability to build AI capabilities that sit natively on top of it.
A strategic product mindset paired with a research sensibility. You read papers, separate signal from hype, tackle loosely defined problems with meticulous attention to detail, and drive ambiguous projects to completion in a fast\-paced dynamic environment without sacrificing trust.
You communicate clearly across cross\-functional teams to influence product strategy, and you evangelize AI engineering practices through workshops, technical playbooks, design guidance, and mentorship that raises the AI fluency of partner organizations.
MS or BS in Computer Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics or a related field OR equivalent practical experience building AI systems in production.
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 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 $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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