Applied AI Engineer

Apple•Cupertino, CA

About The Position

Imagine building AI infrastructure at Apple — systems that power how products are made, how engineers think, and ultimately how hundreds of millions of people experience technology every day. Here is your opportunity to join an engineering team moving Apple's algorithms programs into the AI era. We are developing AI systems that enhance the way engineers work by incorporating human judgment and integrating with data and tools. Our aim is to seamlessly incorporate AI into our processes. We are looking for a software engineer who thrives at the intersection of data systems, AI tooling, and developer experience. You'll design and build platforms that connect AI to the deep organizational context it needs to be genuinely useful — and put those capabilities directly into the hands of the engineers shipping Apple's next-generation sensing technologies. Some of what you build will power internal workflows; some may evolve into products and tools that reach far beyond our team. 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.

Requirements

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
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 (built evaluation pipelines, measured quality, shipped feedback loops)
Sharp problem-solving instincts and a bias toward shipping
BS or MS in Computer Science, Software Engineering, Data Engineering, or equivalent experience

Nice To Haves

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

Responsibilities

Design and build platforms that connect AI to organizational context.
Incorporate human judgment into AI systems.
Integrate AI systems with data and tools.
Build AI-native platforms and data systems.
Develop AI infrastructure to serve algorithms across software, hardware, and design.
Create platforms that enable faster decision-making for cross-functional teams.
Shape the trajectory of internal tools evolving into larger products.
Ship AI-powered tools that real users depend on.

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