Software Development Engineer Ii, Profit Intelligence

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

Software Development Engineer II role focused on building and deploying machine learning models and systems for profit intelligence within Amazon. Responsibilities include production deployment, architecture design, automation of ML pipelines, and metrics design for model effectiveness. The role leverages AWS cloud services and distributed systems.

What you'd actually do

  1. Take responsibility for ensuring that Machine Learning code, models and pipelines are deployed successfully into production, and troubleshooting issues that arise.
  2. Design solution architectures for applications that will use the machine learning models.
  3. Deploy applications to AWS’s cloud leveraging the full spectrum of the AWS cloud services.
  4. Automate model training and testing and deployment via machine learning continuous delivery pipelines.
  5. Design and implement metrics to verify model and algorithm effectiveness.

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
  • Bachelor's degree in computer science or equivalent

What the JD emphasized

  • bleeding edge machine learning models into production

Other signals

  • Deploying ML models into production
  • Designing solution architectures for ML models
  • Automating model training and testing
  • Designing and implementing metrics for model effectiveness