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.
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.
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.
