Senior​/GenAI Engineer

Cupertino, California, United States Software and Services

The Apple Data Platform team powers data analytics, exploration, and feature engineering that fuel Siri, Search, Music, Maps, iCloud and many other beloved products in the Apple ecosystem. Our mission is to provide engineers and data scientists with an innovative, reliable, secure, and user-friendly platform for ingesting, storing, processing, and interacting with data, ultimately helping teams derive insights that drive product success.

As a Staff GenAI Engineer on the Apple Data Platform group’s GenAI Platform team, you’ll play a critical role in shaping and scaling the platform that powers AI application development across Apple. You’ll architect and build core capabilities such as agent orchestration, model selection, prompt and RAG configuration, and evaluation tooling enabling teams to rapidly create, iterate, and deploy GenAI applications.

Your work will also support advanced initiatives like model fine-tuning workflows, and inference optimization, driving a seamless and flexible developer experience for users of the Apple Data Platform.

Join Apple’s Data Platform as a Staff GenAI Engineer, where you'll be at the forefront of delivering transformative AI experiences across Apple products, including Apple Music, TV, Sports and many more. You will play a pivotal role in defining and driving the roadmap for Apple Data Platform's GenAI Platform component, which powers the development, evaluation, and deployment of AI agents and applications.

This includes building foundational capabilities such as agent orchestration, model configuration, prompt tooling, RAG integration, and inference optimization enabling teams across Apple to rapidly create scalable, context-aware GenAI solutions. You will collaborate closely with cross-functional teams of innovative software engineers, product managers, and engineering leaders to continuously evolve the platform for performance, flexibility, and ease of use.

• Define and drive the technical vision, roadmap, and strategy for Apple Data Platform’s GenAI components, enabling scalable development and deployment of AI applications powered by LLMs and agentic workflows
• Guide the design and development of platform capabilities such as agent orchestration, RAG integration, LLM model configuration, prompt tooling, and fine-tuning pipelines
• Drive efforts around LLM inference optimization, including caching, prompt tuning, and latency improvements, ensuring efficient and cost-effective model usage across applications
• Collaborate with engineering, product, and operations teams across Apple to ensure effective adoption of GenAI capabilities in high-impact workflows
• Partner with stakeholders to build reusable AI agents that enhance productivity, automate reasoning tasks, and integrate securely with internal systems and tools
• Mentor new hires and fellow engineers, fostering growth in technical depth and platform mindset
• Establish best practices and processes that ensure engineering excellence, operational sustainability, and a seamless developer experience
• Promote a healthy, inclusive, and innovation-driven team culture with a focus on experimentation, learning, and long-term platform impact

• 8+ years of software development experience
• 3+ years of experience as a technical lead, guiding teams through complex design decisions and setting high benchmarks for code quality, performance, and scalability
• In-depth understanding of large language models (LLMs) and their application in AI-driven solutions, including inferencing, embedding, and knowledge base integration (RAG) for improved data retrieval and contextualization
• Hands‑on experience designing and building GenAI platforms that allow users to create, configure, and deploy AI applications supporting features like agent orchestration, prompt engineering, RAG integration, and model selection
• Experience building AI agents capable of complex multi‑step reasoning and tool usage, with a focus on reliability, traceability, and composability
• Proven experience in fine‑tuning and customizing foundation models to improve task‑specific…