Apple's US Decision Intelligence (DI) team is looking to craft, implement, and operate AI solutions that have a direct and measurable impact on Apple Sales and its customers
Requirements
- 8+ years of experience in Software Development, ML, and/or data science with recent focus on GenAI and LLMs
- Strong JavaScript expertise
- Experienced in Front-end development using React, and API development with Express
- Experience building REST/GraphQL APIs and integrating with external services
- Proficiency in Python (FastAPI, LangChain, or similar frameworks), prompt engineering, and RESTful API design
- Hands-on experience with LLM APIs, embeddings, vector databases, and RAG workflows
- Solid grounding in data structures, async programming, and pipeline orchestration
Responsibilities
- Design modular APIs, SDKs, and microservices to integrate LLMs, retrieval-augmented generation (RAG), traditional ML models, and data pipelines
- Drive interoperability with existing ML systems (e.g., forecasting, attribution, anomaly detection) and support downstream apps like dashboards, web tools, and chat interfaces
- Partner closely with data science, engineering, and sales ops to embed context-aware intelligence in decision-making tools
- Lead technical decision-making on infrastructure components, embedding safety mechanisms (e.g., autonomy sliders, grounding checks, model monitoring)
- Build scalable pipelines for multi-modal agent input, memory, and semantic routing
- Collaborate closely with business teams to incorporate AI into their weekly cadences
- Develop and maintain services that embed GenAI capabilities into production systems
Other
- B.S Degree in Computer Science/Engineering, or equivalent work experience
- Strong time management skills with the ability to collaborate across multiple teams
- Ability to work in a fast-paced, dynamic, constantly evolving business environment
- Familiarity with project management, productivity, and design tools such as Wrike and Sketch
- Sound communication skills - adept at messaging domain and technical content, at a level appropriate for the audience
