Applied AI Engineer

Apple Apple · Big Tech · Cupertino, CA · Sales and Business Development

Applied AI Engineer at Apple Sales, focused on crafting and operating AI solutions using LLMs and agentic workflows for business problems. Responsibilities include designing agentic AI systems, translating research into production, building scalable pipelines, and leading technical decisions on infrastructure and safety mechanisms. Requires PhD or MS with significant experience in applied AI/ML, Python proficiency, and hands-on experience with LLMs, embeddings, vector databases, and agentic workflows.

What you'd actually do

  1. Work at the intersection of applied AI and business operations —translating frontier engineering research into practical, scalable features for AI products.
  2. Design and develop agentic AI systems that reason, plan, and act across tools and modalities.
  3. Translate research into production-ready tools by partnering with platform engineers to productionize your methods into SDKs and APIs.
  4. Prototype and evaluate novel approaches, combining research exploration with hands-on engineering to translate innovations from concept to production.
  5. Build scalable pipelines for multi-modal agent input, memory, and semantic routing.

Skills

Required

  • 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 have

  • 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).

What the JD emphasized

  • strong software development skills
  • passion for applying LLMs and Agentic workflows
  • drive innovation in building scalable ML and AI solutions
  • deep technical expertise in machine learning, statistical modeling, and AI framework development
  • strong problem-solving and interpersonal skills
  • 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
  • architect a full orchestrator and business context layer for sales
  • Proficiency in Python (FastAPI, LangChain, or similar frameworks), context engineering, and RESTful API design
  • 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

Other signals

  • building scalable ML and AI solutions
  • drive innovation
  • translate research into production-ready tools
  • prototype and evaluate novel approaches
  • build scalable pipelines
  • lead technical decision-making on infrastructure components
  • embedding safety mechanisms