Senior Applied Scientist, Sponsored Products and Brands

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

Senior Applied Scientist role focused on architecting and pioneering applied science in multi-modal Generative AI for Amazon's Sponsored Products and Brands advertising platform. The role involves end-to-end innovation from research to production deployment at Amazon scale, with a focus on non-US marketplaces and leveraging technologies like LLMs and semantic hash designs. The candidate should have experience building ML models, deep technical expertise, and a proven track record of delivering value with state-of-the-art technologies in production environments.

What you'd actually do

  1. Architect next-generation systems that replace legacy monolithic infrastructure with scalable, intelligent solutions that scale worldwide on day one.
  2. Pioneer breakthrough applied science in multi-modal GenAI applications for advertising, combining text, image, and multi-lingual support.
  3. Drive end-to-end innovation from research ideation through production deployment at Amazon scale.
  4. Lead cross-functional collaboration with product, engineering, and business teams to translate science into customer impact.

Skills

Required

  • building machine learning models
  • PhD or Master's degree
  • Java
  • C++
  • Python
  • neural deep learning methods
  • machine learning

Nice to have

  • modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy
  • large scale distributed systems such as Hadoop, Spark
  • Recommendations Systems
  • Advertising
  • Information and Retrieval
  • Search domains

What the JD emphasized

  • production-grade experiments
  • delivering value with state-of-the-art technologies at Amazon scale
  • deploying machine learning in production environments
  • building machine learning models for business application experience
  • applied research experience
  • delivering production grade solutions

Other signals

  • Generative AI
  • LLMs
  • Multi-modal
  • Production Deployment
  • Amazon Scale