Applied AI Engineer

AppleCupertino, CA

About The Position

Apple's US Decision Intelligence (DI) team is looking for a talented individual who is passionate about crafting, implementing, and operating AI solutions that have a direct and measurable impact on Apple Sales and its customers. We’re looking for a hands-on Applied AI Engineer with strong software development skills and a passion for applying LLMs and Agentic workflows to real-world business problems. You will strengthen our team’s capabilities in machine learning, and foundational AI development. This role will drive innovation in building scalable ML and AI solutions that enhance our internal AI products intelligence, improve automation, and expand our AI-driven capabilities across business domains. The ideal candidate combines deep technical expertise in machine learning, statistical modeling, and AI framework development with strong problem-solving and interpersonal skills, ensuring effective collaboration and measurable impact in a fast-paced environment.

Requirements

  • PhD in Computer Science, Statistics, Mathematics, AI, or a related quantitative field with 3+ years of experience in applied AI, machine learning, or statistical modeling; or MS with 6+ years of experience in applied AI, machine learning, or statistical modeling.
  • Experience with rapid prototyping, reproduction, and validation of research ideas.
  • Proven ability to translate complex research ideas into scalable, production-level AI solutions.
  • Demonstrated ability to work across the research-to-production spectrum: you have taken experimental or prototype code and made it robust, scalable, and usable by others.
  • Comfort with ambiguity.
  • Ability to architect a full orchestrator and business context layer for sales.
  • Proficiency in Python (FastAPI, LangChain, or similar frameworks), context engineering, and RESTful API design.
  • Ability to build relationships and collaboration opportunities within Channel Sales and with other orgs i.e AIML, SWE etc.
  • Communicate results and insights effectively to partners and senior leaders, as well as both technical and non-technical audiences.
  • Hands-on experience with LLM APIs, embeddings, vector databases, and agentic workflows.
  • Proven experience working with LLMs and GenAI frameworks (LangChain, LlamaIndex, etc.).
  • Solid grounding in data structures, async programming, and pipeline orchestration.
  • Ability to balance competing priorities, long-term projects, and ad hoc requirements in a fast-paced, dynamic, constantly evolving business environment.

Nice To Haves

  • Strong experience articulating and translating business questions into AI solutions.
  • Hands-on industry experience shipping LLM-powered products or features.
  • Experience with personalization, recommendation systems, or commerce intelligence.
  • Experience with anomaly detection and causal inference models.
  • Sound communication skills - adept at messaging domain and technical content, at a level appropriate for the audience.
  • Strong ability to gain trust with stakeholders and senior leadership.
  • Familiarity with embedding, retrieval algorithms, agents, and data modeling for vector development graphs.
  • Other complementary technologies for distributed systems architecture and asynchronous messaging, agent communication, and catching like RabbitMQ, Redis, and Valkey are preferred.
  • Experience working with monitoring and observability tools (e.g., Prometheus, OpenTelemetry, Weights & Biases).

Responsibilities

  • Crafting, implementing, and operating AI solutions that have a direct and measurable impact on Apple Sales and its customers.
  • Applying LLMs and Agentic workflows to real-world business problems.
  • Strengthening team capabilities in machine learning and foundational AI development.
  • Driving innovation in building scalable ML and AI solutions that enhance internal AI product intelligence, improve automation, and expand AI-driven capabilities across business domains.
  • Translating complex research ideas into scalable, production-level AI solutions.
  • Taking experimental or prototype code and making it robust, scalable, and usable by others.
  • Architecting a full orchestrator and business context layer for sales.
  • Building relationships and collaboration opportunities within Channel Sales and with other orgs i.e AIML, SWE etc.
  • Communicating results and insights effectively to partners and senior leaders, as well as both technical and non-technical audiences.
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