Applied AI Engineer

About the position

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

• 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.
• Diagnose whether a production issue is prompt, retrieval, model or data.
• Build AI capabilities that sit natively on top of the data infrastructure ecosystem, SQL engines, lakehouse architectures, workflow orchestration, and streaming systems.
• Communicate clearly across cross-functional teams to influence product strategy.
• Evangelize AI engineering practices through workshops, technical playbooks, design guidance, and mentorship that raises the AI fluency of partner organizations.

Requirements

• 8+ years of software engineering experience building scalable systems, reusable tools and frameworks
• 3+ years taking LLM or agentic systems from prototype to production
• Deep fluency in the modern AI stack
• Solid foundation in machine learning and deep learning
• Understand how modern models (transformers, LLMs) are trained, fine-tuned and evaluated, reason about embeddings, loss functions and statistical rigor
• Proficiency in at least one high-level language (Python, Scala, Java, or Go)
• 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
• Experience with the data infrastructure ecosystem, SQL engines (such as Trino, Presto or Spark), lakehouse architectures, workflow orchestration, and streaming systems
• A strategic product mindset paired with a research sensibility
• Ability to tackle loosely defined problems with meticulous attention to detail, and drive ambiguous projects to completion in a fast-paced dynamic environment without sacrificing trust
• 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

Nice-to-haves

• 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.