Senior Applied AI Engineer, Apple Ads

Summary

At Apple, we focus deeply on our customers’ experience. Apple Ads brings this same approach to advertising, helping people find exactly what they’re looking for and helping advertisers grow their businesses.

Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, and MLS Season Pass. Everything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes, from small app developers to global brands. Because when advertising is done right, it benefits everyone.

The Data Insights organization helps Apple Ads understand, measure, forecast, optimize, and improve the advertising ecosystem across Apple Services. We partner closely with Product, Engineering, Finance, Sales, and Leadership teams to solve complex business and product challenges using data, experimentation, statistical modeling, and machine learning.

We are looking for an Applied AI Engineer to help build and advance AI-powered platforms that transform how teams interact with data and make decisions. This role combines software engineering, machine learning, and agentic AI development to create reliable, scalable systems that make complex analytical capabilities accessible to teams across Apple Ads.

About the role

As an Applied AI Engineer within Data Insights, you will help shape the next generation of AI-powered analytics and decision-support systems for Apple Ads.

You will build and evolve an AI agent platform that connects large language models (LLMs) with internal data infrastructure, analytical tools, and business knowledge to answer complex questions through natural language. Your work will span agent orchestration, context retrieval, evaluation, and production engineering, with a focus on creating reliable, scalable capabilities that other teams can build upon.

Working closely with engineers, data scientists, and business partners, you will help turn emerging AI technologies into practical tools that improve how teams access insights and make decisions.

Responsibilities

• Design and build scalable AI agent systems that connect LLMs with data infrastructure, APIs, and analytical tools.
• Advance agent orchestration, including tool calling, memory, MCPs, context retrieval, and intelligent routing.
• Develop reusable platform components that enable teams to build and extend AI-powered analytical workflows.
• Build evaluation frameworks and monitoring systems to improve agent observability, accuracy, reliability, and performance.
• Apply machine learning and analytical principles to define and improve online and offline metrics that strengthen retrieval quality, reasoning, accuracy, and traceability of AI-generated insights.
• Develop AI-native solutions from concept to production, adhering to first principles and optimizing for scalability, latency, and maintainability.
• Partner with engineers, data scientists, and business stakeholders to translate domain expertise and analytical workflows into reusable AI capabilities.

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, or a related field, or equivalent practical experience.
• Experience designing, building, and deploying production software systems, with strong Python programming and software engineering fundamentals.
• Hands-on experience building and deploying LLM-powered applications using agentic workflows, tool calling, RAG, or related techniques.
• Experience with large-scale data platforms or distributed data systems.
• Solid foundation in AI, machine learning, statistics, and software engineering principles.
• Strong problem-solving, communication, and cross-functional collaboration skills.

Preferred Qualifications

• Experience architecting and scaling production AI agent platforms, including multi-step orchestration, tool execution, memory, and context management.
• Experience developing LLM evaluation frameworks, regression benchmarks, and observability systems to improve agent reliability and reasoning quality.
• Experience with advanced retrieval techniques, embeddings, model fine-tuning, or inference optimization.
• Experience building natural-language-to-SQL systems, analytical agents, or AI-powered decision-support platforms.
• Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field.