Applied Scientist, Sponsored Products Off-search Auction

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

This role focuses on applying GenAI, deep learning, and optimization techniques to improve Amazon's Sponsored Products advertising, specifically in areas like ad retrieval, auctions, and whole-page relevance. The goal is to create personalized and engaging shopping experiences by integrating AI into the advertising lifecycle. The role involves developing and deploying production-ready solutions, staying updated on AI trends, and potentially mentoring junior team members. It emphasizes building AI-powered advertising solutions for consumer-facing platforms.

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
  • Strong foundation in GenAI, large language models, machine learning, deep learning, probabilistic modeling, and/or optimization.
  • 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.
  • Demonstrated ability to communicate complex technical topics clearly to both technical and non-technical audiences.
  • Experience in computational advertising, including familiarity with auction theory, ad economics, and advertiser performance metrics.

What the JD emphasized

  • building models for business application experience
  • developing and deploying models in real-world production environments
  • 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 in computational advertising, including familiarity with auction theory, ad economics, and advertiser performance metrics.

Other signals

  • GenAI
  • LLMs
  • deep learning
  • optimization
  • reinforcement learning
  • production environments
  • web scale
  • computational advertising