Software Development Engineer - Shopping Personalization Ai, Amazon Stores

Amazon Amazon · Big Tech · IL, Tel Aviv · Software Development

Software Development Engineer role focused on building AI systems for Amazon's Shopping Personalization. The role involves designing and implementing features that use generative AI and LLMs to personalize product discovery and recommendations for customers at Amazon scale. Emphasis on end-to-end ownership, scalability, reliability, and collaboration with product and science teams.

What you'd actually do

  1. Design, build, test, and operate production AI features used by multiple teams and operating at Amazon scale
  2. Deliver end-to-end solutions with focus on maintainability, scalability, performance, and reliability
  3. Collaborate with Product and Science to define experiences, run experiments, and iterate based on data
  4. Build AI-powered experiences including personalized recommendations, relevance explanations, and knowledge-driven features using LLMs and generative AI
  5. Define and implement measurement strategies including analytics events and experiment configurations to track engagement and retention

Skills

Required

  • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field
  • 5+ years of non-internship professional software development experience
  • Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design
  • Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
  • Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems

Nice to have

  • Experience building server-side rendered web experiences (SSR) and performance-oriented UI rendering patterns
  • Experience with experimentation (A/B testing), analytics instrumentation, and metrics-driven iteration
  • Familiarity with AI/ML integration and generative AI applications
  • Experience with end-to-end SDLC ownership, including operations and on-call, monitoring/metrics, and incident response/RCA
  • Experience mentoring engineers and driving engineering best practices

What the JD emphasized

  • operate production AI features
  • Amazon scale
  • end-to-end solutions
  • maintainability, scalability, performance, and reliability
  • AI-powered experiences
  • LLMs and generative AI
  • measurement strategies
  • experiment configurations
  • sound technical judgment
  • bias for action
  • ownership of problems end to end
  • operational excellence
  • raising the bar for the team
  • mentoring junior developers
  • advocating engineering best practices

Other signals

  • LLM-driven recommendation experiences
  • AI-native discovery surfaces
  • generative AI
  • large-scale distributed systems
  • Amazon scale