Senior Applied Scientist, Amazon Brand Stores

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

Senior Applied Scientist role focused on building AI-powered brand experiences for Amazon Brand Stores, leveraging generative AI and operating at Amazon scale. The role involves owning the science strategy, hands-on development, defining a multi-year science vision, architecting ahead of the GenAI curve, and rigorous experimentation. It bridges frontier research with high-impact production systems, requiring a blend of science leadership, technical depth, product intuition, and business acumen.

What you'd actually do

  1. Build intelligent systems for Brand Stores. Develop AI-powered solutions leveraging generative models to optimize both advertiser and shopper experiences, measurably improving Brand Store performance.
  2. Define a multi-year science vision and roadmap. Translate customer needs into actionable plans for applied scientists and engineering teams, blending science leadership, technical depth, product intuition, and business acumen.
  3. Architect ahead of the GenAI curve. Anticipate where generative AI is heading over a multi-year horizon and position solutions to capitalize on compounding advances.
  4. Experiment rigorously. Design and run A/B experiments grounded in deep data analysis to validate hypotheses and quantify impact.
  5. Communicate with clarity. Translate complex technical ideas into compelling narratives for both technical and non-technical audiences.

Skills

Required

  • building machine learning models for business application
  • PhD, or Master's degree and 6+ years of applied research experience
  • Programming in Java, C++, Python or related language
  • developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
  • digital advertising technology

Nice to have

  • Experience developing, deploying and managing AI products at scale
  • Experience with generative model architectures
  • Research publications or patents in generative AI technologies
  • Reinforcement Learning from Human Feedback (RLHF)
  • Retrieval-Augmented Generation (RAG)
  • AI model trade-offs (e.g., model size, latency, cost, and output quality)

What the JD emphasized

  • building machine learning models for business application
  • developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
  • Experience developing, deploying and managing AI products at scale
  • generative AI technologies
  • AI model trade-offs

Other signals

  • Generative AI
  • Brand Stores
  • Amazon Scale
  • Frontier Research
  • Production Systems
  • Science Leadership