AI/ML Engineer: System RF Data Ecosystem

Apple • Cupertino, CA

2d

About The Position

Apple’s System RF group designs and characterizes wireless systems across flagship products, while the Smart Data Ecosystem team builds AI/ML-powered analytics for wireless manufacturing and design data. This role focuses on architecting, developing, and deploying scalable AI solutions that improve engineering productivity and support hardware design. The engineer will bridge hardware workflows with artificial intelligence by building agents that automate repetitive tasks, analyze high-dimensional data, surface trends, and use historical design knowledge to help engineers make decisions more quickly and accurately. The work involves integrating LLMs through cloud APIs and secure on-premise deployments; enabling agents to use APIs, databases, and internal tools; developing RAG pipelines and fine-tuning models; designing multi-agent systems; and bringing prototypes into robust, production-scale internal services.

Requirements

• At least 7 years of experience in AI/ML-related projects, including a track record of architecting and deploying production-scale Generative AI solutions.
• Master’s or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, or a related field.
• Expert-level knowledge of multi-agent system design, including orchestration, coordination, and persistent state management.
• Strong command of agent communication protocols such as MCP and A2A, and frameworks for distributed agentic workflows.
• Expert software development skills and a strong foundation in architectural design and scalable, modular systems.
• Ability to lead cross-functional architecture discussions and translate product goals into robust technical designs.
• At least 10 years of professional experience in AI/ML-related projects, demonstrating innovation and technical leadership.
• Ability to build efficient agent interfaces for APIs, high-dimensional databases, and custom toolsets.
• Proficiency in Python, including core and asynchronous development, and strong command of FastAPI or Flask.
• Experience deploying Generative AI solutions for complex, high-dimensional, domain-specific engineering data.
• Experience overseeing the full lifecycle of large-scale software applications in enterprise-grade production environments.
• Deep understanding of model selection, intelligent routing, and fallback strategies across multiple LLM providers.
• Experience developing rigorous Gen AI evaluation systems for measuring agent performance, safety, and accuracy.

Responsibilities

• Integrate state-of-the-art LLMs, including through cloud-based APIs and secure on-premise deployments.
• Develop safe, efficient mechanisms for agents to invoke APIs, query high-dimensional databases, and interact with internal engineering tools.
• Architect and maintain RAG pipelines and oversee LLM fine-tuning for domain-specific engineering data.
• Lead the development of multi-agent architectures, including orchestration, coordination, and persistent state management.
• Transition AI solutions from prototypes to robust, production-scale internal services handling complex, high-volume engineering requests.
• Build intelligent agents to automate repetitive engineering tasks, analyze high-dimensional data, surface trends, and leverage historical design intelligence.
• Lead cross-functional architecture discussions and translate product goals into technical system designs.
• Develop interfaces that enable agents to use APIs, databases, and custom toolsets.
• Implement Gen AI evaluation systems to assess deployed agents’ performance, safety, and accuracy.
• Apply model selection, intelligent routing, and fallback strategies across LLM providers.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior