AI/ML Engineer: System RF Data Ecosystem

Summary

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 Aminutepple’s flagship products—including iPhone, Watch, iPad, Mac, and Audio—ensuring 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.

Join a team operating at the intersection of hardware, data, and AI—architecting intelligent software tools that solve complex system optimization problems where you can directly influence the performance of Apple products used worldwide!

About the role

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.

Responsibilities

• 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 deployments.
• Orchestrate Tool-Use and Integration: Develop safe and efficient mechanisms for agents to invoke APIs, query high-dimensional databases, and interact with custom internal engineering tools.
• Optimize 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 data.
• Design and Architect Multi-Agent Systems: Lead the development of sophisticated multi-agent architectures, specifically focusing on complex orchestration, coordination, and persistent state management.
• Scalable Deployment: Oversee the transition of AI solutions from experimental prototypes to robust, production-scale internal services that handle complex, high-volume engineering requests.

Minimum Qualifications

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

Preferred Qualifications

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