Applied AI Engineer

You will work alongside machine learning scientists, algorithm engineers, hardware teams, designers, and human factors researchers to build the AI-native platforms and data systems that accelerate how Apple ships its next generation of devices. We're a small, high-impact group with an ambitious roadmap — you'll have outsized influence on the architecture and direction of what we build. The algorithms behind Apple's most beloved features span software, hardware, and design — and the AI infrastructure you build will serve all of them. You'll be at the center of a uniquely cross-functional environment where world-class talent in each discipline depends on the platforms you create to move faster and make better decisions. The best ideas here have a way of starting as internal tools and growing into something bigger — you'll help shape that trajectory. You might have built a chatbot that needed to reason over internal docs, designed an agent that orchestrated multi-step tool calls, or created a pipeline that turned unstructured data into searchable knowledge. What matters is that you've gone beyond tutorials — you've shipped AI-powered tools that real users depend on. 3+ years of software engineering experience Proficiency in Python and at least one of: Swift, TypeScript, or another systems-level language Experience designing and building backend services, data pipelines, or platform infrastructure Hands-on experience building with modern AI/agent frameworks Strong software architecture and API design sensibilities — you think in systems, not scripts Experience building retrieval pipelines (RAG, embeddings, vector search) or knowledge retrieval systems Strong communicator who works effectively across disciplines Experience evaluating and iterating on AI system outputs — you've built evaluation pipelines, measured quality, and know that shipping AI means shipping feedback loops Sharp problem-solving instincts and a bias toward shipping BS or MS in Computer Science, Software Engineering, Data Engineering, or equivalent experience. Experience building macOS or iOS client applications Background in workflow orchestration, experiment tracking, or reproducibility tooling Experience designing data models, storage layers, or integration APIs for complex domains Experience integrating LLMs with external tools and APIs (tool-use patterns, MCP, function calling) or building custom integrations between LLMs and external systems Familiarity with access-controlled or policy-aware data systems Experience with vector databases or embedding pipelines (Pinecone, Chroma, pgvector, or similar) Track record building developer tools, CLIs, or internal platforms that engineers rely on daily Experience with cloud AI services (AWS Bedrock, Azure OpenAI, Google Vertex AI) or self-hosted model serving Contributions to open-source projects in AI/ML infrastructure Comfort navigating large, established codebases and shipping iteratively within them