Applied AI Engineer

Build AI agent and analytics capabilities for Apple Ads (Data Insights), connecting LLMs to internal data and decision-support workflows.

Responsibilities

• Design and develop scalable AI agent systems that connect LLMs with data infrastructure, APIs, and analytical tools
• Improve agent orchestration capabilities, including tool calling, memory, MCPs, context retrieval, and intelligent routing
• Create reusable platform components that let teams build and extend AI-powered analytical workflows
• Develop evaluation frameworks and monitoring systems to increase observability, accuracy, reliability, and performance
• Use machine learning and analytical methods to define and enhance online and offline metrics that improve retrieval quality, reasoning accuracy, and traceability of AI-generated insights
• Deliver AI-native solutions from concept to production with a focus on scalability, latency, and maintainability
• Collaborate with engineers, data scientists, and business stakeholders to convert domain expertise and analytical workflows into reusable AI capabilities

Requirements

• Experience architecting and scaling production AI agent platforms, including multi-step orchestration, tool execution, memory, and context management
• Experience building LLM evaluation frameworks, regression benchmarks, and observability systems to improve agent reliability and reasoning quality
• Experience with advanced retrieval methods such as embeddings, and/or model fine-tuning, and/or inference optimization
• Experience building natural-language-to-SQL systems, analytical agents, or AI-enabled decision-support platforms
• Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field
• 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 deploying LLM-powered applications using agentic workflows, tool calling, RAG, or related techniques
• Experience with large-scale data platforms or distributed data systems
• Strong foundation in AI, machine learning, statistics, and software engineering principles
• Strong problem-solving, communication, and cross-functional collaboration skills

Technologies

• Python
• LLMs
• MCPs
• RAG
• Tool calling
• Embeddings
• Model fine-tuning
• Inference optimization
• Natural-language-to-SQL

Benefits

• Comprehensive medical and dental coverage
• Retirement benefits
• Discounted products and free services
• Reimbursement for certain educational expenses, including tuition
• Opportunity to become an Apple shareholder through Apple’s discretionary employee stock programs
• Discretionary restricted stock unit awards eligibility
• Ability to purchase Apple stock at a discount with voluntary participation in the Employee Stock Purchase Plan
• Discretionary bonuses or commission payments (as applicable)
• Relocation (as applicable)

Pay

• Base pay range: USD 150,400 - 277,600 per year
• Base pay depends on skills, qualifications, experience, and location

Apple employees may be eligible for discretionary restricted stock unit awards and can purchase Apple stock at a discount if participating in the Employee Stock Purchase Plan. This role might be eligible for discretionary bonuses or commission payments and relocation, subject to plan eligibility requirements and other terms.