Applied AI Engineer - iCloud Data

Cupertino, California, United States Software and Services

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?

The iCloud Data organization within Apple Services enables iCloud users to access all their content across apps (Photos, Mail, Messages, Face Time, 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.

• 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.

• 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…