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 is your opportunity to join an engineering team moving Apples 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 are looking for a software engineer who thrives at the intersection of data systems AI tooling and developer experience. Youll 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 Apples 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. Were a small high-impact group with an ambitious roadmap youll have outsized influence on the architecture and direction of what we algorithms behind Apples most beloved features span software hardware and design and the AI infrastructure you build will serve all of them. Youll 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 youll help shape that 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 youve gone beyond tutorials youve shipped AI-powered tools that real users depend on.

Build novel AI-powered platforms that fundamentally change how engineers explore analyze and act on complex program data nDesign data infrastructure that brings together diverse knowledge sources - making the right context available to AI and engineers at the right time with appropriate access controls nCreate reproducible analysis workflows with full provenance tracking so that AI-generated insights can be trusted iterated on and shared across disciplines nDevelop agentic pipelines that automate multi-step research and analysis while keeping engineers in the loop to steer and validate nBuild infrastructure for continuous automated intelligence from CI-integrated analysis to background monitoring that surfaces what matters nDesign collaboration systems that help cross-functional teams (SW HW Design Human Factors) accumulate and leverage shared institutional knowledge nPartner with algorithm teams shipping features for iPhone Apple Watch iPad and future products to understand their data challenges and deliver tooling that compounds in value over time

3 years of software engineering experiencenProficiency in Python and at least one of: Swift TypeScript or another systems-level languagenExperience designing and building backend services data pipelines or platform infrastructurenHands-on experience building with modern AI/agent frameworks nStrong software architecture and API design sensibilities you think in systems not scriptsnExperience building retrieval pipelines (RAG embeddings vector search) or knowledge retrieval systemsnStrong communicator who works effectively across disciplinesnExperience evaluating and iterating on AI system outputs youve built evaluation pipelines measured quality and know that shipping AI means shipping feedback loopsnSharp problem-solving instincts and a bias toward shippingnBS or MS in Computer Science Software Engineering Data Engineering or equivalent experience.

Experience building macOS or iOS client applicationsnBackground in workflow orchestration experiment tracking or reproducibility toolingnExperience designing data models storage layers or integration APIs for complex domainsnExperience integrating LLMs with external tools and APIs (tool-use patterns MCP function calling) or building custom integrations between LLMs and external systemsnFamiliarity with access-controlled or policy-aware data systemsnExperience with vector databases or embedding pipelines (Pinecone Chroma pgvector or similar)nTrack record building developer tools CLIs or internal platforms that engineers rely on dailynExperience with cloud AI services (AWS Bedrock Azure OpenAI Google Vertex AI) or self-hosted model servingnContributions to open-source projects in AI/ML infrastructurenComfort navigating large established codebases and shipping iteratively within them