Principal Applied Scientist, Advertiser Growth, Amazon Sponsored Products & Brands

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

This role leads the development of generative AI applications for advertisers, focusing on agentic experiences for recommendations and guidance. It involves fine-tuning, reinforcement learning, and preference optimization, with a strong emphasis on creating customer-facing products and mentoring AI talent.

What you'd actually do

  1. define and lead the science roadmap of developing agentic experiences for recommendations and guidance delivered to +1.6MM Sponsored Products and Brand advertisers across multiple channels (Ad Console, Sales-managed, and 3P partners).
  2. develop methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale.
  3. deliver customer-facing products that directly help them create, optimize, and grow their campaigns.
  4. elevate the team’s scientific and technical rigor, identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling.
  5. communicate learnings to leadership and mentor and grow Applied AI talent across the Ads Org.

Skills

Required

  • PhD in Computer Science, Statistics or related field.
  • 5+ years of applied ML experience in building customer-facing recommender systems involving AI, ML, and NLP.
  • Hands-on experience with designing and prototyping AI solutions for real world applications.

Nice to have

  • Research publications or patents in generative AI technologies
  • Experience with natural language processing and generative model architectures
  • Background in advertising technology or e-commerce platforms
  • Demonstrated track record of innovative AI solution development

What the JD emphasized

  • leading 50+ builders
  • agentic AI applications
  • agentic experiences
  • fine-tuning
  • reinforcement learning
  • preference optimization
  • evaluation frameworks
  • safety, reliability, and trust at scale
  • customer-facing products
  • scientific and technical rigor
  • best-in-class algorithms, methodologies, and infrastructure
  • rapid experimentation and scaling
  • mentor and grow Applied AI talent

Other signals

  • leading 50+ builders
  • developing agentic experiences
  • customer-facing products
  • elevating scientific and technical rigor
  • mentor and grow Applied AI talent