Principal Applied Scientist, Sponsored Products and Brands

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

Principal Applied Scientist role focused on developing and deploying generative AI solutions for Amazon's Sponsored Products and Brands advertising platform. The role involves defining science vision, building ML/LLM models for advertiser and shopper experiences, optimizing campaign performance, and leading scientific rigor. Requires strong ML, LLM, and GenAI expertise with experience in production systems and digital advertising.

What you'd actually do

  1. Develop AI solutions for Sponsored Brands advertiser and shopper experiences. Build monetization and optimization systems that leverage generative models to value and improve campaign performance.
  2. Define a long-term science vision and roadmap for our Sponsored Brands advertising business, driven from our customers' needs, translating that direction into specific plans for applied scientists and engineering teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.
  3. Design and conduct A/B experiments to evaluate proposed solutions based on in-depth data analyses.
  4. Effectively communicate technical and non-technical ideas with teammates and stakeholders.
  5. Stay up-to-date with advancements and the latest modeling techniques in the field.

Skills

Required

  • PhD in Computer Science, Statistics or related field.
  • 10+ years of programming in Java, Python or related language.
  • 10+ years of applied ML experience in building complex, real-time systems involving AI, ML with successful delivery to customers.
  • 3+ years in LLMs, model fine tuning and prompt engineering.
  • 3+ years in digital advertising technology.

Nice to have

  • Demonstrated track record of innovative AI solution development.
  • Experience with generative model architectures.
  • Research publications or patents in generative AI technologies.
  • Experience in Reinforcement Learning from Human Feedback (RLHF), Retrieval-Augmented Generation (RAG) and AI model trade-offs (e.g., model size, latency, cost, and output quality).

What the JD emphasized

  • 10+ years of applied ML experience in building complex, real-time systems involving AI, ML with successful delivery to customers.
  • 3+ years in LLMs, model fine tuning and prompt engineering.

Other signals

  • Develop AI solutions for Sponsored Brands advertiser and shopper experiences.
  • Build monetization and optimization systems that leverage generative models to value and improve campaign performance.
  • Define a long-term science vision and roadmap for our Sponsored Brands advertising business, driven from our customers' needs, translating that direction into specific plans for applied scientists and engineering teams.