Applied AI & Data Engineer - Business & Education

Shape how Applied AI and modern data engineering work together to support Apple’s Business and Education products.

Responsibilities

• Own the data engineering for Apple Business and Education offerings, spanning pipelines and lakehouse architecture through analytics reporting
• Build AI-native capabilities including agents, intelligent workflows, and self-serve analytics that change how Data Engineering, Analytics, and Data Science teams operate
• Architect, build, and operate production AI products that combine LLMs, foundation models, agents, and deterministic components for both human and machine consumers
• Spend time writing code, prototyping, and shipping alongside the team, not just reviewing work
• Focus on measuring system quality and improving it instead of debating opinions
• Design and build scalable data platforms using cloud-native patterns across the warehouse/SQL engine and the model-serving layer
• Deliver ETL/ELT, streaming, and CDC pipelines with workflow orchestration, data quality checks, observability, and alerting to prevent downstream breakage in analytics and AI
• Provide technical leadership through architecture and design reviews, implementation guidance, production-readiness reviews, and mentoring engineers while prioritizing across concurrent initiatives
• Communicate clearly across cross-functional teams to influence strategy and raise partner organizations’ AI fluency via workshops, playbooks, and design guidance

Requirements

• 8+ years across data engineering, analytics engineering, software engineering, or ML engineering, including 3+ years building and shipping Applied AI and agentic LLM systems in production
• Experience architecting, building, and operating production AI products composed of LLMs, foundation models, agents, and deterministic components for human and machine consumers
• Judgment on inference vs. compute, decomposition across specialized models, orchestration of multi-step reasoning and tool use, and system degradation when a model fails
• Hands-on fluency with LLM and agent frameworks including LangChain, LlamaIndex, Semantic Kernel, Google ADK, or equivalent
• Experience with vector search and RAG pipelines, including pgvector, FAISS, Pinecone, or equivalent; multi-agent coordination; tool invocation; and stateful reasoning
• Production discipline for AI systems: evaluation harnesses, guardrails, telemetry (offline evals, golden sets, LLM-as-judge, behavioral regression, drift monitoring)
• Optimization experience for cost, latency, throughput, and inference quality: model selection, serving decisions, token-spend control, caching, batching, streaming, distillation, quantization, and speculative decoding
• Foundation in machine learning and deep learning: transformers and LLM training, fine-tuning, evaluation; embeddings, loss functions, and statistical rigor; ability to diagnose whether failures are prompt, retrieval, model, or data related
• Scalable data platform experience using cloud-native patterns (Databricks, AWS, or equivalent) and comfort with the warehouse/SQL engine (Trino, Presto, Spark) and the model-serving layer
• Proficiency in at least one high-level language: Python, Scala, Java, or Go; strong SQL; code that is readable, observable in production, and testable at boundaries
• Experience delivering ETL/ELT, streaming, and CDC pipelines with Spark, Kafka, and Delta Lake, including workflow orchestration, data quality checks, observability, and alerting for batch and real-time data
• Knowledge of data modeling patterns and ability to choose the right approach based on tradeoffs in analytical performance, governance, and extensibility
• Track record of hands-on technical leadership: architecture/design reviews, implementation guidance, production-readiness reviews, and mentoring engineers (3+ years) while prioritizing across concurrent initiatives
• Clear communication across cross-functional teams to influence strategy and deliver workshops, playbooks, and design guidance to increase AI fluency
• Product mindset with research sensibility: separate signal from hype, handle loosely defined problems with detail, and drive completion while maintaining trust
• BS or MS in a relevant field (Computer Science, Information Systems, Artificial Intelligence, Machine Learning, Engineering, Mathematics, Statistics) or equivalent practical experience building data and AI systems in production

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

• Model and prompt customization at scale: fine-tuning foundation models; training reward models; building custom retrieval, reranking, or embedding models for domain-specific tasks; prompt engineering for performance, reliability, and safety
• MLOps and LLMOps experience: model lifecycle management, deployment pipelines, observability, and prompt and evaluation versioning
• Experience building natural-language interfaces over data, including text-to-SQL, semantic search, or analytics copilots (internal or customer-facing)
• Experience using AI-native code editors and agent-assisted development environments; establishing guardrails for responsible use across security, IP protection, compliance, and code quality
• Experience with Google Cloud or Azure; stream-processing (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

Location: Seattle, WA (onsite)

Salary: USD 175,000 - 308,500 per year