Applied Scientist, Sponsored Products Off-search Homepage Team

Amazon Amazon · Big Tech · Palo Alto, CA · Applied Science

This role focuses on applying Generative AI and LLMs to transform ad experiences on Amazon's homepage and other surfaces, impacting product discovery and customer engagement. It involves building and deploying models for ad retrieval, auctions, and personalized shopping experiences, operating across the full stack from backend systems to the user-facing layer.

What you'd actually do

  1. Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences.
  2. Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life.
  3. Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization.
  4. Contribute to the enhancement of team’s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling.
  5. Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team.

Skills

Required

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • 3+ years of building models for business application experience
  • Experience programming in Java, C++, Python or related language
  • Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience in machine learning, data mining, information retrieval, statistics or natural language processing
  • Experience developing and deploying models in real-world production environments.

Nice to have

  • Proven expertise in Generative AI, foundation models, LLMs, and/or fine-tuning and customization for downstream tasks.
  • Hands-on experience in ads ranking, retrieval, recommendation systems, search, or personalization at web scale.
  • Deep understanding of multi-modal modeling, few-shot learning, retrieval-augmented generation (RAG), or reinforcement learning from human feedback (RLHF).
  • Experience with online experimentation, A/B testing frameworks, and metrics design for advertising or e-commerce.

What the JD emphasized

  • production-ready science solutions
  • rapid experimentation and scaling
  • real-world production environments

Other signals

  • GenAI
  • LLMs
  • personalization
  • recommendation systems
  • full stack