Software Development Engineer, Amazon Private Brands

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

Software Development Engineer II role focused on building internal and external facing customer experiences and Gen AI solutions for product lifecycle management within Amazon Private Brands. The role involves designing, developing, and maintaining code, collaborating with teams, and identifying opportunities to leverage generative AI for efficiency and product enhancement.

What you'd actually do

  1. Design, develop, and maintain efficient, reusable, and reliable code
  2. Implement complex software solutions that meet business requirements
  3. Collaborate with cross-functional teams to define, design, and ship new features
  4. Identify and resolve performance bottlenecks and bugs
  5. Participate in architecture and design reviews to provide technical guidance

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
  • 1+ years of software development engineer or related occupational experience
  • 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
  • 1+ years of Object Oriented Design experience
  • Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field
  • Experience programming with at least one software programming language

Nice to have

  • 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
  • Experience creating processes and improving tools that have measurably improved productivity, quality, or advertiser experience
  • Bachelor's degree or above in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field, or experience in defining and creating benchmarks for assessing GenAI model performance
  • Usage of generative AI tools to enhance workflow efficiency, with a willingness to learn effective prompting and evaluation practices.
  • Ability to recognize opportunities where generative AI could enhance products, workflows, or customer experiences.

What the JD emphasized

  • Gen AI solutions