Applied AI Engineer

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.

DESCRIPTION

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.

MINIMUM QUALIFICATIONS

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.

PREFERRED QUALIFICATIONS

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