Would you like to help shape how AI and modern data engineering & analytics come together to power Apple's Business and Education products, at a scale that touches millions of enterprises, schools, and the devices and services they depend on?
Apple's Business and Education organization builds the infrastructure, platforms, and services behind Apple's offerings for enterprise and education customers: device management, identity, and subscription services, and the classroom apps built on top of them. Our team owns the data engineering behind all of it, from pipelines and lakehouse architecture through analytics reporting, and we are building a new generation of AI-native capabilities on top: agents, intelligent workflows, and self-serve analytics that change how our Data Engineering, Analytics, and Data Science teams work. The ambition is to define what an AI-first data organization looks like at Apple scale.
We are looking for a well-rounded builder. You spent the earlier part of your career deep in software, data, or ML engineering, and the last few years applying that foundation to ship Applied AI products end-to-end. You think architecturally, you know where LLMs and agents earn their keep and where deterministic code is the better answer, and you would rather measure a system's quality than argue about it.
Minimum Qualifications
- 8+ years across data engineering, analytics engineering, software engineering, or ML engineering, with the last 3+ years building and shipping Applied AI and agentic LLM systems in production. You are still a builder: you want to spend real time writing code, prototyping, and shipping alongside your team, not only reviewing what others ship.
- You architect, build, and operate production AI products composed of LLMs, foundation models, agents, and deterministic components, for both human and machine consumers. You have clear judgment on where to infer and where to compute, how to decompose tasks across specialized models, how to orchestrate multi-step reasoning and tool use, and how the system degrades when a model fails.
- Hands-on fluency with modern LLM and agent frameworks (LangChain, LlamaIndex, Semantic Kernel, Google ADK, or equivalent), vector search (pgvector, FAISS, Pinecone, or equivalent), RAG pipelines, multi-agent coordination, tool invocation, and stateful reasoning. You have moved past vanilla RAG: you know where retrieval breaks, and when to reach for planning, reranking, structured reasoning, fine-tuning, or plain deterministic code 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).
- A foundation in machine learning and deep learning. You understand how transformers and LLMs are trained, fine-tuned, and evaluated, you reason about embeddings, loss functions, and statistical rigor, and you can tell whether a production failure is prompt, retrieval, model, or data.
- You design and build scalable data platforms on modern cloud-native patterns (Databricks, AWS, or equivalent), and you are as comfortable in the warehouse and the SQL engine (Trino, Presto, Spark) as in the model-serving layer. Proficiency in at least one high-level language (Python, Scala, Java, or Go), strong SQL, and the discipline to write code that is readable, observable in production, and testable at the boundaries.
- Experience delivering ETL/ELT, streaming, and CDC (change data capture) pipelines with technologies such as Spark, Kafka, and Delta Lake, for both batch and real-time data, along with the workflow orchestration, data quality checks, observability, and alerting that catch breakage before it reaches downstream analytics or AI systems.
- Knowledge of data modeling patterns and the judgment to pick the right one for a given use case, trading off analytical query performance, governance, and extensibility.
- A track record of hands-on technical leadership: architecture and design reviews, implementation guidance, production-readiness review, and 3+ years mentoring engineers and prioritizing across concurrent initiatives. You communicate clearly enough across cross-functional teams to influence strategy, and you raise the AI fluency of partner organizations through workshops, playbooks, and design guidance.
- A product mindset paired with a research sensibility. You read papers, separate signal from hype, work loosely defined problems with meticulous attention to detail, and drive them to completion without sacrificing trust in the result.
- BS or MS in Computer Science, Information Systems, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, or a related field, or equivalent practical experience building data and 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 optimized for performance, reliability, and safety.
- 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 internal or customer-facing use.
- Experience using AI-native code editors and agent-assisted development environments to improve developer productivity, and establishing guardrails for their responsible use across security, IP protection, compliance, and code quality.
- Experience with Google Cloud or Azure, stream-processing systems (Apache Flink, Spark Streaming, Kafka Streams), and NoSQL or analytics datastores (Cassandra, MongoDB, Druid, Apache Pinot) for real-time data and real-time AI applications.
- Experience building AI, machine learning, and experimentation systems in regulated or privacy-sensitive environments.
- Contributions to open source, research, talks, or technical writing that have shaped how others build AI systems.
- Prior experience leading or managing engineers, or serving as technical lead across multiple concurrent data and AI projects.