Applied AI & Data Engineer - Business & Education

Help shape how AI and modern data engineering come together to power Apple’s Business and Education products.

Responsibilities

• Own the data engineering for Apple Business and Education offerings, spanning pipelines, lakehouse architecture, and analytics reporting.
• Develop AI-native capabilities including agents, intelligent workflows, and self-serve analytics that shift how Data Engineering, Analytics, and Data Science teams operate.
• Architect, build, and operate production AI products composed of LLMs, foundation models, agents, and deterministic components for both human and machine consumers.
• Write production code, prototype, and ship alongside the team, not limited to code review.
• Evaluate system quality using measurable criteria rather than debate.
• Build scalable data platforms using cloud-native patterns, working comfortably across the warehouse and SQL engine and the model-serving layer.
• Deliver ETL/ELT, streaming, and CDC pipelines with workflow orchestration, data quality checks, observability, and alerting to catch issues before downstream analytics or AI use.
• Provide hands-on technical leadership through architecture and design reviews, implementation guidance, and production-readiness reviews; mentor engineers and prioritize across concurrent initiatives.
• Communicate across cross-functional teams to influence strategy and raise AI fluency through workshops, playbooks, and design guidance.

Requirements

• 8+ years across data engineering, analytics engineering, software engineering, or ML engineering.
• Last 3+ years building and shipping Applied AI and agentic LLM systems in production.
• Architect, build, and operate production AI products using LLMs, foundation models, agents, and deterministic components for human and machine consumers.
• Strong judgment on when to infer vs. compute, how to decompose work across specialized models, how to orchestrate multi-step reasoning and tool use, and how systems degrade when a model fails.
• Hands-on fluency with LLM and agent frameworks: LangChain, LlamaIndex, Semantic Kernel, Google ADK, or equivalent.
• Vector search experience with pgvector, FAISS, Pinecone, or equivalent; experience with RAG, multi-agent coordination, tool invocation, and stateful reasoning.
• Production discipline for AI systems including evaluation harnesses, guardrails, and telemetry (offline evals, golden sets, LLM-as-judge, behavioral regression, drift monitoring).
• Optimize for cost, latency, throughput, and inference quality (model selection, serving decisions, token-spend control, caching, batching, streaming, distillation, quantization, speculative decoding).
• Foundation in machine learning and deep learning: understanding transformers and LLM training, fine-tuning, and evaluation; reasoning about embeddings, loss functions, and statistical rigor; ability to diagnose whether failures come from prompt, retrieval, model, or data.
• Cloud-native data platform experience using Databricks, AWS, or equivalent; comfortable across warehouse/SQL engines (Trino, Presto, Spark) and model-serving layers.
• Proficiency in at least one language: Python, Scala, Java, or Go; strong SQL; build readable, observable, testable production code.
• Experience delivering ETL/ELT, streaming, and CDC pipelines using Spark, Kafka, and Delta Lake (batch and real-time), plus orchestration, data quality checks, observability, and alerting.
• Knowledge of data modeling patterns and judgment to select the right pattern with tradeoffs across analytical performance, governance, and extensibility.
• Track record of hands-on technical leadership: architecture and design reviews, implementation guidance, production-readiness review; 3+ years mentoring engineers and prioritizing across concurrent initiatives.
• Clear cross-functional communication to influence strategy and raise AI fluency via workshops, playbooks, and design guidance.
• Product mindset paired with research sensibility: differentiate signal from hype, handle loosely defined problems with detailed execution, and deliver without sacrificing trust.
• Education: BS or MS in a relevant field (Computer Science, Information Systems, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics, or related) or equivalent practical experience.

Technologies

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

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

• Fine-tuning and customization at scale: fine-tuning foundation models, reward model training, custom retrieval, reranking, or embedding models for domain-specific tasks, and prompt engineering optimized for performance, reliability, and safety.
• MLOps and LLMOps: model lifecycle management, deployment pipelines, observability, and prompt and evaluation versioning.
• Natural-language interfaces over data (text-to-SQL, semantic search, analytics copilots) for internal or customer-facing use.
• Experience with AI-native code editors and agent-assisted development environments, including guardrails for 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 AI applications.
• Experience building AI/ML/experimentation systems in regulated or privacy-sensitive environments.
• Open source, research, talks, or technical writing that shaped how others build AI systems.
• 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 • Minimum experience: 8 years