Senior Applied Scientist, Catalog System Services Science

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

This role focuses on applying advanced GenAI, multimodal learning, and agentic architectures to enrich Amazon's product catalog at a massive scale. The scientist will formulate research problems, design and implement models, pioneer explainable AI, own ML pipelines from research to production, and mentor others. The role emphasizes working with frontier models and agents to solve complex catalog understanding challenges involving text, images, and structured data.

What you'd actually do

  1. Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval. In essence, translating ambiguous business challenges into tractable scientific frameworks
  2. Design and implement leading models leveraging frontier models, and agentic architectures to enrich catalog information at billion-product scale
  3. Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions
  4. Own end-to-end ML pipelines from research ideation to production deployment, processing petabytes of multimodal data with rigorous evaluation frameworks
  5. Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements

Skills

Required

  • 4+ years of applied research experience
  • 3+ years of building machine learning models for business application experience
  • PhD, or Master's degree and 6+ years of applied research experience
  • Experience programming in Java, C++, Python or related language
  • Experience with neural deep learning methods and machine learning

Nice to have

  • Experience with training and deploying machine learning systems to solve large-scale optimizations
  • Prior experience in the domains of LLMs, foundation models, or large-scale deep learning systems
  • Publications in top-tier venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, EMNLP, ACL, NAACL, COLING, KDD, SIGMOD, WWW, AAAI, or similar
  • Experience with multimodal LLMs, including hands-on post training techniques

What the JD emphasized

  • Frontier Models
  • Agents
  • multimodal learning
  • large-scale information retrieval
  • Explainable AI
  • production deployment
  • GenAI
  • VLM

Other signals

  • Frontier Models
  • Agents
  • Multimodal
  • Large-scale information retrieval
  • Explainable AI
  • End-to-end ML pipelines
  • Production deployment