Staff Applied Scientist

Adobe Adobe · Enterprise · San Jose, CA

Staff Applied Scientist at Adobe focused on building foundational generative multimodal models (image and video) through end-to-end training pipelines, data curation, and distributed training. The role involves leading core development in pre-training areas, partnering with research and other teams, and improving model quality and controllability. Requires strong expertise in Vision-Language Models and diffusion-based architectures, with an emphasis on publications and industry experience.

What you'd actually do

  1. Design and implement end-to-end training pipelines to build foundational model for both images and videos.
  2. Lead core development for specific pre-training areas (e.g., text to image and text to video), while aligning with broader team strategy.
  3. Develop scalable workflows for data curation, data quality improvements, and distributed training.
  4. Partner closely with research, data, evaluation, infrastructure, pre-training and post-training teams to push the editing quality for both images and videos.
  5. Closely collaborate with both pre-training and post-training team to understand the model’s capability and limitations to propose actionable solutions to improve quality.

Skills

Required

  • Ph.D. in Computer Science, Machine Learning, or a related field preferred
  • Proven track record in pre-training of large-scale multimodal models, specifically on cross modality for image and video data
  • Deep understanding of pre-training for multimodal generative models
  • Strong expertise in Vision-Language Models (VLMs), including experience with contrastive learning, multimodal alignment, and leveraging VLM-based encoders to improve semantic understanding in generative tasks
  • Deep understanding of modern diffusion-based architectures (DiT)
  • Ability to design and implement scalable pipelines for data curation, data quality control, and distributed training in collaboration with data and infrastructure teams
  • Experience optimizing model inference and deployment for high-throughput product environments, ensuring a balance between generative quality and computational efficiency
  • strong publications experience
  • previous industry level intern experience

Nice to have

  • Ph.D. in Computer Science, Machine Learning, or a related field

What the JD emphasized

  • materially improve the quality and controllability of Adobe’s generative multimodal models
  • strengthening Adobe’s competitive position in generative AI quality and alignment
  • Proven track record in pre-training of large-scale multimodal models
  • strong publications experience

Other signals

  • design and implement end-to-end training pipelines
  • pre-training of large-scale multimodal models
  • improve instruction-following, visual fidelity, and edit consistency