Applied Scientist Ii, Alexa Ads

Amazon Amazon · Big Tech · IN, KA, Bengaluru · Applied Science

The Applied Scientist II role at Amazon's Alexa Ads team focuses on building Generative AI powered agentic advertising products and personalization models for the Alexa consumer assistant. This involves designing, developing, and evaluating ML models for NLP, recommendation systems, and personalization, conducting data analysis, building ML pipelines, running A/B experiments, and collaborating with engineers to deploy models into production. The role is part of a greenfield team aiming to rethink ad ranking, pricing, and personalization for voice-first and screen-first surfaces, with opportunities for both production deployment and top-tier publications.

What you'd actually do

  1. Design, develop, and evaluate innovative machine learning and deep learning models for natural language processing (NLP), recommendation systems, and personalization.
  2. Conduct hands-on data analysis and build scalable ML pipelines.
  3. Design and run A/B experiments to measure the impact of new models on customer experience and ad performance.
  4. Collaborate with software development engineers to deploy models into high-scale, real-time production environments.

Skills

Required

  • 3+ years of building models for business application experience
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience programming in Java, C++, Python or related language
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development

What the JD emphasized

  • build machine learning models that seamlessly and naturally integrate relevant advertising into the Alexa experience while deeply personalizing user interactions
  • rethinking how ads are ranked, priced, and personalized across voice-first and screen-first surfaces
  • problems that don't have textbook solutions
  • shape the science roadmap, pick the problems, and define the culture from day one
  • Direct business impact — your models directly drive revenue
  • Ship AND Publish: We encourage top-tier publications (NeurIPS, ACL, EMNLP, KDD, ICML, WWW) while ensuring your research hits production.

Other signals

  • Generative AI
  • Agentic Advertising
  • Conversational AI
  • Personalization
  • NLP
  • Recommendation Systems