Applied AI Engineer

Summary

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.

Minimum Qualifications

BS in Computer Science, Software Engineering, Data Engineering, or related degree 3+ years of s relevant industry expeience 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

Preferred Qualifications

MS or PhD n Computer Science, Software Engineering, Data Engineering, or related degree 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

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.

Responsibilities

Build novel AI-powered platforms that fundamentally change how engineers explore, analyze, and act on complex program data Design 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 Create reproducible analysis workflows with full provenance tracking, so that AI-generated insights can be trusted, iterated on, and shared across disciplines Develop agentic pipelines that automate multi-step research and analysis while keeping engineers in the loop to steer and validate Build infrastructure for continuous, automated intelligence — from CI-integrated analysis to background monitoring that surfaces what matters Design collaboration systems that help cross-functional teams (SW, HW, Design, Human Factors) accumulate and leverage shared institutional knowledge Partner 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