Full Stack Software Engineer - Camera & Photos Tools & AI Team

Apple Apple · Big Tech · Cupertino, CA +1 · Software and Services

Full Stack Software Engineer for Apple's Camera & Photos Tools & AI Team. This role involves building internal tools that support imaging engineering and quality workflows, with a focus on integrating AI/ML capabilities like multimodal and vision models, LLM-powered interfaces, and agentic workflows. The engineer will work across the full stack (Swift, React, Python) and be responsible for the end-to-end development, reliability, and maintainability of these AI-powered features.

What you'd actually do

  1. Design and build AI-powered features within internal tools, including LLM integrations, agentic workflows, and vision model pipelines for automated image quality analysis.
  2. Evaluate, integrate, and maintain AI/ML models in production: monitoring for quality regression, managing model versions, and balancing cost, latency, and accuracy tradeoffs across the service lifecycle.
  3. Develop prompt engineering strategies and retrieval-augmented systems (RAG) that make internal image and metadata corpora accessible and actionable to partner teams.
  4. Leverage AI coding assistants and productivity tooling to accelerate development cycles and raise overall team velocity.
  5. Plan, design, implement, and own Swift applications used by imaging engineers and quality teams.

Skills

Required

  • Swift
  • Python
  • JavaScript/TypeScript
  • REST API design
  • React
  • SwiftUI
  • integrating AI/ML models
  • asynchronous job execution patterns
  • software engineering fundamentals
  • communication skills

Nice to have

  • LLM APIs
  • prompt design
  • context window management
  • output validation
  • graceful degradation
  • multimodal models
  • computer vision models
  • image analysis
  • quality assessment
  • visual data retrieval

What the JD emphasized

  • shipping production software
  • integrating AI/ML models (LLMs, vision models, or similar) into production software systems, not just as a user but as a builder responsible for reliability and maintainability
  • AI-powered features to the same engineering standards as any other production code

Other signals

  • building AI-native tooling
  • integrating multimodal and vision models
  • designing LLM-powered interfaces
  • embedding AI into reliable, maintainable engineering systems
  • AI coding assistants