Applied Scientist Ii, Sponsored Products and Brands

Amazon Amazon · Big Tech · NY +1 · Applied Science

Applied Scientist II role at Amazon Ads focusing on Sponsored Products and Brands, specifically the Search Ranking and Interleaving (R&I) team. The role involves developing and applying GenAI and ML techniques to optimize ad ranking, allocation, and relevance on the search page. Key responsibilities include building real-time ML algorithms for ad auctions, developing multi-objective optimization methods, and researching new ML approaches to improve shopper experience and drive revenue. The role requires experience in building models for business applications and applying theoretical models in practice.

What you'd actually do

  1. Solve challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon.
  2. Drive end-to-end GenAI & Machine Learning projects that have a high degree of ambiguity, scale, complexity.
  3. Develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions.
  4. Develop efficient algorithms for multi-objective optimization using deep learning methods to find operating points for the ad marketplace then evolve them
  5. Research new and innovative machine learning approaches.

Skills

Required

  • building models for business application experience
  • 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
  • Experience applying theoretical models in an applied environment

Nice to have

  • Experience building machine learning models or developing algorithms for business application
  • Knowledge of architectural concepts and algorithms, schedule tradeoffs and new opportunities with technical team members
  • Experience implementing algorithms using both toolkits and self-developed code

What the JD emphasized

  • building models for business application experience
  • applying theoretical models in an applied environment

Other signals

  • develop real-time machine learning algorithms
  • allocate billions of ads per day
  • multi-objective optimization using deep learning methods
  • research new and innovative machine learning approaches