Applied Scientist, Core Search

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

This role focuses on building and improving ML-powered search ranking models at Amazon's scale, leveraging large datasets and exploring techniques like reinforcement learning to enhance customer experience. The primary deliverable is the shipped AI product (search ranking system).

What you'd actually do

  1. build search ranking models that work for thousands of product types, billions of queries, and hundreds of millions of customers spread around the world
  2. find the next set of big improvements to ranking
  3. leverage large datasets to understand the complexities of customer behavior
  4. build ML models that work at Amazon scale
  5. building ML models, analyzing data from your recent A/B tests, and guiding teams on best practices

Skills

Required

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • 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

  • ML algorithms
  • ML at Amazon's scale
  • search ranking models
  • ML models that work at Amazon scale
  • reinforcement learning
  • improving search ranking systems
  • building ML models
  • machine learning for better ranking
  • reinforcement learning to bring the benefits of exploration to search ranking
  • infrastructure to make it all happen at scale and efficiently

Other signals

  • ML algorithms
  • ML at Amazon's scale
  • search ranking models
  • large datasets
  • ML models at Amazon scale
  • exploration techniques
  • reinforcement learning
  • improving search ranking systems
  • building ML models
  • analyzing data from A/B tests
  • ML scientists and software engineers
  • machine learning for better ranking
  • reinforcement learning to bring the benefits of exploration to search ranking
  • infrastructure to make it all happen at scale and efficiently