Interested in this AI/ML Engineer role at Apple?
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About This Role
The Apple Cloud AI team powers data and AI capabilities that fuel Music, TV, App Store, Maps, iCloud and many other beloved products in the Apple ecosystem. Our mission is to provide engineers and data scientists with an innovative, reliable, secure, and user-friendly platform for Agentic and Generative AI development and deployment at scale. As a Senior GenAI Engineer on the Apple Cloud AI group’s Agent Platform team, you’ll play a critical role in shaping and scaling the platform that powers AI application development across Apple. You’ll architect and build core capabilities such as agentic harness, agent orchestration, model selection, building services for agent context and knowledge management, and evaluation tooling enabling teams to rapidly create, iterate, and deploy GenAI applications. Your work will also support advanced initiatives like multi-agentic systems, long horizon agents, model fine-tuning and reinforcement learning workflows, and inference optimization, driving a seamless and flexible developer experience for users of Apple Cloud AI. Join us and be a part of the Data and AI Platform journey at Apple, where your contributions will impact Apple's customers and countless teams within Apple.
Description
Join Apple’s Cloud AI as a Senior GenAI Engineer, where you'll be at the forefront of delivering transformative AI experiences across Apple products, including Apple Music, TV, Sports and many more. You will play a pivotal role in defining and driving the roadmap for Apple Cloud GenAI and Agentic Platform component, which powers the development, evaluation, and deployment of AI agents and applications. This includes building foundational capabilities such as agent orchestration, model configuration, prompt tooling, RAG integration, and inference optimization enabling teams across Apple to rapidly create scalable, context-aware GenAI solutions. You will collaborate closely with cross-functional teams of innovative software engineers, product managers, and engineering leaders to continuously evolve the platform for performance, flexibility, and ease of use.
","responsibilities":"Design and development of platform capabilities such as agent orchestration, agentic RAG, LLM model configuration, prompt optimization tooling, and fine-tuning and RL pipelines that meet the highest standard in security and safety
Drive efforts around LLM and Agent optimization, including caching, prompt tuning, and latency improvements, ensuring efficient and cost-effective model usage across agentic systems
Define and drive the technical roadmap, and strategy for Apple Cloud AI GenAI components, enabling scalable and secure development and deployment of agentic systems and harnesses
Collaborate with engineering, product, and operations teams across Apple to ensure effective adoption of GenAI capabilities in high-impact workflows
Partner with stakeholders to build reusable AI agents that enhance productivity, automate reasoning tasks, and integrate securely with internal systems and tools
Mentor new hires and fellow engineers, fostering growth in technical depth and platform mindset
Establish best practices and processes that ensure engineering excellence, operational sustainability, and a seamless developer experience
Promote a healthy, inclusive, and innovation-driven team culture with a focus on experimentation, learning, and long-term platform impact
Preferred Qualifications
Expertise in 3rd party Cloud Environments (Google Cloud and AWS)
Hands on experience in using Kubernetes as orchestration layer
Proven experience in customizing foundation models to improve task-specific performance and domain alignment
Minimum Qualifications
4+ years of machine learning and software development experience
1+ years of experience as a technical lead, guiding teams through complex design decisions and setting high benchmarks for code quality, performance, and scalability
Hands-on experience designing and building GenAI platforms that allow users to create, configure, and deploy AI Agents supporting features like agent orchestration, prompt engineering, RAG integration, and model selection
Experience building AI agents capable of complex multi-step reasoning and tool usage, with a focus on reliability, traceability, and composability
Experience building and deploying Agent Harnesses, including integrations with Agent Memory and Context, MCP and tools, and runtime secure environments (Agent Sandboxes)
In-depth understanding of security primitives for AI Agents and Harnesses including policy enforcement, MCP/tools access control, runtime security, drift and hallucination detection for long horizon agents.
In-depth understanding of large language models (LLMs) and their application in AI-driven solutions, including inferencing, embedding, and knowledge base integration (RAG) for improved data retrieval and contextualization
Knowledge of LLM inference optimization techniques, including prompt tuning, caching, quantization, and latency reduction across different model families
Strong programming skills in Python, Java, or similar languages, with an emphasis on AI/ML systems development and platform engineering
Demonstrated ability to work cross-functionally and influence product development through a combination of technical leadership and user-centered thinking
Passion for operational excellence, automation, and delivering scalable, developer-friendly AI infrastructure
B.S, M.S. or PhD Degree in Computer Science/Engineering, or equivalent work 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 $150,400 and $277,600, 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 $150K-$277K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $150K to $277K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Apple AI Hiring
Apple has 38 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Safety, AI Product Manager. Positions span Cupertino, CA, US, Sunnyvale, CA, US, Zurich, Zurich, Switzerland. Compensation range: $194K - $401K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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