Applied AI & Data Engineer - Business & Education

Apple’s Business and Education products rely on reliable data platforms and production-grade AI capabilities that can scale with demand. In this role, you will help shape how AI-native data engineering and analytics come together, from lakehouse foundations to agentic systems and self-serve reporting used at Apple scale.

What you’ll do

• Own the data engineering behind Apple Business and Education offerings, covering pipelines and lakehouse architecture through analytics reporting.
• Build AI-native capabilities on top of modern data foundations, including agents, intelligent workflows, and self-serve analytics that change how Data Engineering, Analytics, and Data Science teams work.
• Architect, build, and operate production AI products composed of LLMs, foundation models, agents, and deterministic components for both human and machine consumers.
• Write code in real time, prototype, and ship alongside the team rather than focusing only on reviews.
• Measure system quality with evaluation-focused approaches rather than debating outcomes.
• Design and build scalable data platforms using modern cloud-native patterns, working across the warehouse and SQL engine as well as the model-serving layer.
• Deliver ETL/ELT, streaming, and CDC pipelines with workflow orchestration, data quality checks, observability, and alerting that catch breakage before downstream analytics or AI systems.
• Provide hands-on technical leadership through architecture and design reviews, implementation guidance, and production-readiness reviews, while mentoring engineers and prioritizing across concurrent initiatives.
• Communicate clearly across cross-functional teams to influence strategy and raise AI fluency using workshops, playbooks, and design guidance.

Core experience and qualifications

• 8+ years across data engineering, analytics engineering, software engineering, or ML engineering, with 3+ years building and shipping Applied AI and agentic LLM systems in production.
• Experience architecting, building, and operating production AI products using LLMs, foundation models, agents, and deterministic components for human and machine consumers.
• Strong judgment on when to infer versus compute, how to decompose tasks across specialized models, how to orchestrate multi-step reasoning with tool use, and how to handle degradation when a model fails.
• Hands-on fluency with modern LLM and agent frameworks such as LangChain, LlamaIndex, Semantic Kernel, Google ADK, or equivalents.
• Vector search experience including pgvector, FAISS, Pinecone, or equivalents; plus RAG pipelines, multi-agent coordination, tool invocation, and stateful reasoning.
• Production discipline for AI systems, including evaluation harnesses and guardrails (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).
• Foundational understanding of machine learning and deep learning, including transformers and LLM training, fine-tuning, and evaluation; ability to reason about embeddings, loss functions, and statistical rigor, and to diagnose production failures (prompt, retrieval, model, or data).
• Experience designing and building scalable cloud-native data platforms (including Databricks, AWS, or equivalents), with comfort in both the warehouse/SQL engine (such as Trino, Presto, Spark) and the model-serving layer.
• Proficiency in at least one high-level language: Python, Scala, Java, or Go, plus strong SQL and the ability to write readable, observable, testable production code.
• Experience delivering ETL/ELT, streaming, and CDC pipelines (including Spark, Kafka, Delta Lake) for both batch and real-time, with orchestration, data quality checks, observability, and alerting.
• Knowledge of data modeling patterns and the judgment to select the right approach with trade-offs across query performance, governance, and extensibility.
• Track record of hands-on technical leadership: architecture and design reviews, implementation guidance, production-readiness reviews, plus 3+ years mentoring and prioritizing across multiple concurrent initiatives.
• Clear cross-functional communication to influence strategy and improve partner organizations’ AI fluency through workshops, playbooks, and design guidance.
• A product mindset alongside a research sensibility: separate signal from hype, work on loosely defined problems with meticulous attention to detail, and drive completion without sacrificing trust in results.
• 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.

Technologies you may work with

• LangChain, LlamaIndex, Semantic Kernel, Google ADK
• pgvector, FAISS, Pinecone
• Databricks lakehouse architecture, Databricks, AWS
• Trino, Presto, Spark
• Python, Scala, Java, Go
• Kafka, Delta Lake
• LLMs, foundation models, agents, RAG, multi-agent coordination, tool invocation, vector search

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses (including tuition) for formal education related to advancing your career
• Opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs
• Eligible for discretionary restricted stock unit awards
• Can purchase Apple stock at a discount if voluntarily participating in the Employee Stock Purchase Plan
• Discretionary bonuses or commission payments as well as relocation (where eligible)

Preferred qualifications

• Model and prompt customization at scale, including fine-tuning foundation models, training reward models, custom retrieval, reranking, embedding models for domain tasks, and prompt engineering optimized for performance, reliability, and safety.
• Experience with MLOps and LLMOps, including model lifecycle management, deployment pipelines, observability, and versioning for prompts and evaluations.
• Experience building natural-language interfaces over data, such as 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, including guardrails for 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 influenced how others build AI systems.
• Prior experience leading or managing engineers, or serving as technical lead across multiple concurrent data and AI projects.

Location: Seattle, WA (onsite). Salary: USD 175,000 - 308,500 per year.