Applied Scientist

Adobe Adobe · Enterprise · San Jose, CA

Research Scientist role focused on training large-scale generative AI models for image and video synthesis, emphasizing conditional generation, editing, and improving instruction compliance, controllability, and visual clarity. The role involves collaborating cross-functionally, developing evaluation pipelines, and translating research into production-ready implementations.

What you'd actually do

  1. Conduct innovative research and development in training large-scale generative AI models for image and video synthesis.
  2. Develop, carry out, and carefully assess sophisticated techniques for conditional generation and editing. Emphasize improving instruction compliance, controllability, and visual clarity in pre-trained image and video models.
  3. Collaborate cross-functionally with researchers, engineers, and product teams to translate multimodal innovations into scalable downstream applications coordinated within Adobe products.
  4. Develop and maintain robust evaluation pipelines to assess generative models across quality, efficiency, robustness, and safety metrics.
  5. Translate research concepts and published work into production-ready implementations using Python and modern machine learning frameworks.

Skills

Required

  • Master’s or Ph.D. degree in Computer Science, Machine Learning, or a related field
  • Extensive practical experience in large-scale generative AI training concentrating on image and video generation and editing
  • Familiarity with diffusion models, transformers, or other brand new generative architectures
  • Excellent communication skills and ability to collaborate across cross-functional teams
  • Strong coding and prototyping ability in Python, PyTorch, and ML infrastructure tools
  • Working with product teams on technology transfers
  • Strong history of publishing in Computer Science, AI/ML, or related areas

What the JD emphasized

  • extensive practical experience in large-scale generative AI training concentrating on image and video generation and editing
  • Strong history of publishing in Computer Science, AI/ML, or related areas

Other signals

  • large-scale generative AI training
  • image and video synthesis
  • conditional generation and editing
  • multimodal innovations
  • production-ready implementations