AIML Engineer System RF Data Ecosystem

At Apple new ideas quickly transform into products services and customer experiences that delight millions. This innovation is fueled by cutting-edge hardware developed within the Hardware Engineering Group. As a vital part of this organization the System RF group designs and characterizes wireless systems across Apples flagship productsincluding iPhone Watch iPad Mac and Audioensuring world-class performance from prototype to production. Within this organization the Smart Data Ecosystem team empowers product evolution by building AI/ML-powered analytics that unlock critical insights from complex wireless manufacturing and design data. The team is currently seeking a Senior AI Development Engineer to architect develop and deploy scalable AI solutions internally. nnJoin a team operating at the intersection of hardware data and AIarchitecting intelligent software tools that solve complex system optimization problems where you can directly influence the performance of Apple products used worldwide!

This role is dedicated to transforming engineering productivity and enabling cutting-edge hardware design through the strategic application of machine learning and generative AI. As a Senior AI Engineer you will bridge the gap between complex hardware engineering workflows and state-of-the-art artificial intelligence. You will architect intelligent agents capable of automating repetitive engineering tasks analyzing high-dimensional data to surface hidden trends and tapping into decades of historical design intelligence. By building these systems you will empower engineers to arrive at critical decisions with unprecedented speed and accuracy directly influencing the next generation of Apple innovation.

Integrate Large Language Models: Direct the integration of state-of-the-art LLMs (such as Anthropic Claude Mistral and Gemini) via both cloud-based APIs and secure on-premise Tool-Use and Integration: Develop safe and efficient mechanisms for agents to invoke APIs query high-dimensional databases and interact with custom internal engineering Retrieval and Knowledge: Architect and maintain advanced RAG (Retrieval-Augmented Generation) pipelines and oversee LLM fine-tuning processes to align models with domain-specific engineering and Architect Multi-Agent Systems: Lead the development of sophisticated multi-agent architectures specifically focusing on complex orchestration coordination and persistent state Deployment: Oversee the transition of AI solutions from experimental prototypes to robust production-scale internal services that handle complex high-volume engineering requests.

7 years of experience in AI/ML-related projects with a proven track record of architecting and deploying production-scale Generative AI or PHD in Artificial Intelligence Machine Learning Computer Science or a related -level knowledge of multi-agent system design including sophisticated orchestration coordination and persistent state command of Agent Communication Protocols (e.g. MCP A2A) and frameworks for designing distributed agentic -level software development skills with a solid foundation in architectural design principles and the creation of scalable modular ability to lead cross-functional architecture discussions and translate ambitious product goals into robust technical system designs.

10 years of professional experience in AI/ML-related projects demonstrating a long-term track record of innovation and technical ability to build efficient interfaces for agents to invoke APIs query high-dimensional databases and interact with custom in Python (Core and Async) and a strong command of FastAPI or Flask for serving high-performance AI success in deploying Generative AI solutions tailored to complex high-dimensional and domain-specific engineering experience overseeing the full lifecycle of large-scale software applications deployed within an enterprise-grade production understanding of model selection intelligent routing and fallback strategies to ensure reliability across multiple LLM developing and implementing rigorous Gen AI evaluation systems to measure the performance safety and accuracy of deployed agents