Applied AI Engineer iCloud Data

Would you like to drive the future of Apples 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 worldn

The iCloud Data organization within Apple Services enables iCloud users to access all their content across apps (Photos Mail Messages FaceTime Calendar Enterprise u0026 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 this excites you and youre energized by taking novel AI techniques from research to production on hard high-leverage high-scale problems wed love to hear from you! Were 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.n

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 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 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 deep domain expertise across our data and AI stack product and business and be an advocate for engineering excellence and responsible 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 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.n

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 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 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 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 -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. Youve 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 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).nExperience 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 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 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 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.

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 with MLOps and LLMOps model lifecycle management deployment pipelines observability and prompt and evaluation building natural-language interfaces over data text-to-SQL semantic search or analytics copilots for both internal and customer-facing use 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).nExperience 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 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.