Applied Scientist, Personalize, Aws

Amazon Amazon · Big Tech · Seattle, WA · Machine Learning Science

Applied Scientist role focused on building state-of-the-art recommendation systems and personalization engines at scale for AWS. The role involves developing innovative solutions, publishing research, and working with large-scale data and computational resources. Key scientific topics include large-scale recommendation and ranking models, sequential and session-based recommendation, cold-start and few-shot personalization, contextual and real-time recommendations, representation learning, and integrating foundation models/LLMs into recommendation pipelines.

What you'd actually do

  1. Do you want to work on building state-of-the-art recommendation systems and personalization engines at scale to transform how millions of customers discover products, content, and experiences?
  2. develop the science that powers personalized experiences for countless businesses in cloud computing!
  3. You will have the opportunity to partner with technology and business teams to solve real-world personalization challenges, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world.
  4. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and journals.

Skills

Required

  • 3+ years of building models for business application experience
  • 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

  • publish your findings at peer reviewed conferences and journals
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals

Other signals

  • state-of-the-art recommendation systems
  • personalization engines at scale
  • publish your findings at peer reviewed conferences and journals