Applied Scientist Ii, Cia Orchestration Team

Amazon Amazon · Big Tech · Austin, TX · Machine Learning Science

Applied Scientist II role focused on developing and deploying machine learning models and data-driven solutions for operational efficiency in Amazon's Fulfillment Centers and Transportation Sites. The role involves working with diverse datasets and ML frameworks to create predictive models, optimization algorithms, and intelligent automation, with a focus on scalability and production readiness. The team specifically handles network device management through a user-friendly, auditable mechanism for provisioning, configuration, and policy management.

What you'd actually do

  1. Partner with Systems Engineers, Network Engineers, and Software Engineers globally to research, design, and deploy machine learning models and data-driven solutions that improve efficiency across our Fulfillment Centers and Transportation Sites.
  2. Work with a wide variety of datasets, ML frameworks, and research methodologies — spanning third-party platforms, open-source tooling, and proprietary Amazon systems — to develop and validate scientific approaches to complex operational problems.
  3. Technicians and Engineers worldwide will look to you for scientific guidance, analytical insights, and innovative modeling solutions to problems that are truly unique to Amazon's scale and operations.
  4. In your day-to-day work, you will focus on developing and applying predictive models, optimization algorithms, and intelligent automation to enable our business to scale effectively with growing customer demand.

Skills

Required

  • building models for business application
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience programming in Java, C++, Python or related language
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • develop scalable, high-performing solutions that require minimal long-term maintenance
  • rigor, reproducibility, simplicity, and scalability in our science
  • well-grounded in research and built to perform reliably in production environments

Other signals

  • develop scalable, high-performing solutions
  • research, design, and deploy machine learning models
  • develop and validate scientific approaches
  • develop and applying predictive models, optimization algorithms, and intelligent automation