Applied Scientist, Global Risk Intelligence and Prevention, Seller Abuse Prevention

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

This role focuses on building and deploying scalable AI solutions to detect and prevent marketplace abuse on Amazon's platform. It involves using advanced AI techniques, including LLMs and agents, to analyze massive-scale, multi-modal datasets and develop predictive risk detection models. The role also includes developing interpretability pipelines, defining detection strategies, and partnering with engineering teams for production deployment and evaluation.

What you'd actually do

  1. Design and build predictive risk detection models using advanced AI techniques, including Natural Language Processing including LLMs and agents to proactively identify bad actors and prevent marketplace abuse at scale
  2. Own the end-to-end scientific solution from risk quantification through decision optimization, determining the appropriate actions to take across varying risk levels
  3. Develop interpretability and reasoning pipelines that provide transparent, actionable explanations for model decisions to support enforcement and seller experience
  4. Work with risk programs across the seller lifecycle to define detection strategies, translate operational investigation patterns into automated systems, and prioritize high-impact risk areas
  5. Partner with engineering teams to deploy models into production, define evaluation frameworks, and collaborate with operations and verification teams to measure and improve detection effectiveness

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

  • advanced AI solutions
  • transform how we prevent bad actors
  • proactively detect and prevent marketplace abuse
  • massive-scale, multi-modal datasets
  • advanced AI techniques
  • LLMs and agents
  • end-to-end scientific solution
  • interpretability and reasoning pipelines
  • automated systems
  • deploy models into production
  • evaluate detection effectiveness
  • emerging abuse vectors
  • graph, computer vision, NLP, and anomaly detection methodologies

Other signals

  • building risk detection models
  • proactively detect and prevent marketplace abuse
  • scalable AI solutions
  • massive-scale, multi-modal datasets
  • LLMs and agents