Applied Scientist, Prime Video - Generative AI (video)

Amazon Amazon · Big Tech · Sunnyvale, CA · Machine Learning Science

Applied Scientist role focused on building and deploying generative AI models for content creation within Prime Video, aiming to transform entertainment experiences. The role involves end-to-end ML project ownership, from research and prototyping to optimization and deployment, with a focus on production-ready content generation and localization.

What you'd actually do

  1. Build generative AI models that create production-ready content, including movie content, localized assets, and visual marketing materials used across Prime Video's global platform.
  2. Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, complexity.
  3. Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models.
  4. Research new and innovative machine learning approaches.
  5. Share knowledge and research outcomes via internal and external conferences and journal publications

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 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

  • deep knowledge in one of: graphics, deep learning, generative AI and/or reinforcement learning
  • experience applying them real-world problems
  • take calculated risks in developing rapid prototypes and iterative model improvements
  • effectively translate technical findings into production systems and business action (and customer delight)

Other signals

  • build generative AI models
  • transform entertainment experiences
  • apply them real-world problems
  • develop rapid prototypes and iterative model improvements
  • productize and maintain the associated solutions