Staff Applied Scientist, Generative Ai/ml

Adobe Adobe · Enterprise · San Jose, CA +3

Staff Applied Scientist role focused on Generative AI research and development for visual, audio, and multi-modal outputs. The role involves pioneering research, developing novel generative AI technologies, and training/fine-tuning large foundation models across various modalities. Emphasis on publishing research and collaborating with ML engineers to integrate technologies into Adobe products.

What you'd actually do

  1. Conduct pioneering research and development in Generative AI for visual (image/video/3D), audio, and multi-modal outputs.
  2. Develop and deploy novel generative AI technologies to existing and new Adobe Products.
  3. Research and develop novel large-scale foundation models with deep reasoning and world-building capabilities.
  4. Collaborate with world-class researchers and ML engineers to bring research ideas to creative workflows used by millions.
  5. Publish and present your work in world-class scientific venues in CV/AI/ML/CG fields

Skills

Required

  • Ph.D. in Computer Science, CV/AI/ML/CG or related fields
  • 4+ years professional experience
  • Experience in training Generative AI models (pre-training and/or post-training)
  • Expertise in large-scale model training and optimization
  • Data curation
  • Distributed training
  • Memory-efficient techniques
  • Post-training techniques (fine-tuning, alignment, distillation)
  • Modern deep learning frameworks (e.g., PyTorch)
  • Scaling models on GPU/TPU clusters
  • Excellent communication skills
  • Teamwork

Nice to have

  • Experience on large-scale generative model training
  • Experience on synthetic data generation
  • Experience of working with large-scale datasets
  • Experience of working with product teams on technology transfers

What the JD emphasized

  • Ph.D. in Computer Science, CV/AI/ML/CG or related fields and 4+ years professional experience
  • Research or industry experience in training Generative AI models (pre-training and/or post-training) in at least one of the following modalities: image, video, 3D, or audio.
  • Expertise in large-scale model training and optimization, including data curation, distributed training, and memory-efficient techniques.
  • Experience with post-training techniques such as fine-tuning, alignment or distillation.
  • Proficiency with modern deep learning frameworks (e.g., PyTorch) and experience scaling models on GPU/TPU clusters.

Other signals

  • Generative AI
  • foundation models
  • multimodal