Applied Scientist I, Alexa Ads

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

Applied Scientist role focused on building Generative AI models for conversational ads and personalization within the Alexa ecosystem. The role involves designing, developing, and evaluating deep learning models for NLP and recommendation systems, building ML pipelines, running A/B experiments, and deploying models to production. The team is greenfield, aiming for direct business impact and encouraging top-tier publications alongside production deployment.

What you'd actually do

  1. Design, develop, and evaluate innovative deep learning and GenAI 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

  • Experience programming in Java, C++, Python or related language
  • Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
  • Bachelor's degree or above in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields
  • 1+ years of building models for business application experience

Nice to have

  • Experience implementing algorithms using both toolkits and self-developed code
  • Have publications at top-tier peer-reviewed conferences or journals
  • Master's degree in CS, CE, ML or related field experience

What the JD emphasized

  • build machine learning models
  • deep learning and GenAI models
  • NLP
  • recommendation systems
  • personalization
  • scalable ML pipelines
  • deploy models into high-scale, real-time production environments
  • Greenfield team
  • shape the science roadmap
  • own your problem space end-to-end
  • Ship AND Publish

Other signals

  • Generative AI
  • personalization
  • recommendation systems
  • NLP
  • production deployment
  • A/B testing