Applied Scientist, Generative Ai/ml

Adobe Adobe · Enterprise · San Jose, CA +3

Research scientist role focused on generative AI for visual, audio, and multi-modal outputs. Involves pioneering research, development, and deployment of novel generative AI technologies, including large-scale foundation models with deep reasoning capabilities. Requires expertise in training and fine-tuning models across various modalities and proficiency in deep learning frameworks.

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
  • 1+ years professional experience
  • Training Generative AI models (pre-training and/or post-training)
  • Image, video, 3D, or audio modalities
  • 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
  • Great teammate

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 1+ 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
  • large-scale model training
  • multi-modal outputs