Software Development Engineer, Prime Science

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

Software Development Engineer role focused on building and scaling AI/ML foundations and microservices for Amazon Prime, supporting real-time decisions and large-scale training/inference workflows. Collaborates with scientists and other engineers to productionize ML models and deliver customer-facing experiences.

What you'd actually do

  1. Design and build science products that scale to 100's of millions of customers
  2. Work alongside a multi-disciplinary team to deliver with high quality on hard science problems
  3. Interface with internal customers to build science products that solve their needs
  4. Grow more junior engineers and be force multiplier
  5. Identify operational challenges early in the system and drive operational excellence

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

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
  • Bachelor's degree in computer science or equivalent
  • 2+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience
  • Experience in machine learning, data mining, information retrieval, statistics or natural language processing
  • Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems

What the JD emphasized

  • productionize ML models
  • large-scale AI/ML training and inference workflows
  • real-time decisions
  • single-digit millisecond latency requirements

Other signals

  • productionize ML models
  • build scalable AI/ML foundations
  • develop highly available micro services that power real-time decisions
  • build scalable offline systems for large-scale AI/ML training and inference workflows