Software Development Engineer Ii, Personalization

Amazon Amazon · Big Tech · Seattle, WA · Software Development

Software Development Engineer II role focused on building and improving personalization and recommendation features for Amazon's consumer platform, leveraging machine learning and big data technologies.

What you'd actually do

  1. Design, develop, and maintain scalable software solutions to process big data.
  2. Collaborate with diverse stakeholders to envision, design, develop, and launch new and impactful software
  3. Utilize technology to solve complex problems
  4. Deliver high-quality code on schedule
  5. Demonstrate proficiency in diverse data structures and algorithms, making informed decisions on their appropriate usage

Skills

Required

  • 3+ years of non-internship professional software development experience
  • 2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
  • Experience programming with at least one software programming language
  • Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
  • Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution

Nice to have

  • 3+ years of full software development cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Bachelor's degree in computer science or equivalent
  • Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques

What the JD emphasized

  • Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
  • Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques

Other signals

  • personalization
  • recommendations
  • big data
  • machine learning