Applied Scientist, Prime Video - Generative AI

Amazon Amazon · Big Tech · NY +1 · Machine Learning Science

Applied Scientist role focused on building and deploying generative AI models for content creation in Prime Video. This role involves end-to-end ML project ownership, from research and experimentation to optimization and deployment at scale, with a focus on production-ready outputs like movie content, localized assets, and marketing materials.

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. Run experiments, gather data, and perform statistical analysis.
  5. Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving.

Skills

Required

  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • 3+ years of building models for business application experience
  • Experience programming in Java, C++, Python or related language
  • Experience in generative models (diffusion, flow, transformers)
  • Hands-on experience with image/video synthesis and editing techniques
  • Proficiency in PyTorch and modern DL toolkits (e.g., Hugging Face ecosystem)

Nice to have

  • Experience in professional software development
  • Publications in top-tier AI/ML/Graphics Conferences (CVPR, ICCV/ECCV, SIGGRAPH, NeurIPS, ICLR)
  • Experience with controllable generation methods, including emerging approaches (familiarity with LoRA/ControlNet, parameter-efficient tuning, or test-time training a plus)
  • Expertise in one or more of: harmonization, relighting, style transfer, lip-sync, segmentation, matting, depth estimation, 3D camera/scene modeling.

What the JD emphasized

  • production-ready content
  • end-to-end machine learning projects
  • deploy your models
  • scalable, efficient, automated processes
  • production-ready systems at Amazon scale

Other signals

  • Build generative AI models that create production-ready content
  • Drive end-to-end machine learning projects
  • Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models
  • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving
  • Research new and innovative machine learning approaches