Applied AI Engineer - iCloud Data

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?

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.