Applied Scientist, 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. Responsibilities include designing, developing, and evaluating ML models for NLP, recommendation systems, and personalization, conducting data analysis, building ML pipelines, running A/B experiments, and collaborating on production deployment.

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 with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution, or experience debugging, profiling, and implementing best software engineering practices in large-scale systems

Nice to have

  • Experience with and deep understanding of advertising technologies (i.e., ad server, DSP)
  • Experience (technical and operational) with multiple domain areas of programmatic advertising technologies (DSP, RTB, bid shading, machine learning optimization, ad verification, ad tracking, ad attribution, etc.)
  • Experience building large-scale machine learning models and infrastructure for online recommendation, ads ranking, personalization, or search

What the JD emphasized

  • building models for business application experience
  • patents or publications at top-tier peer-reviewed conferences or journals
  • Machine Learning and Large Language Model fundamentals

Other signals

  • Generative AI
  • personalization
  • recommendation systems
  • NLP
  • production deployment