Sr. Applied Scientist

Adobe Adobe · Enterprise · San Jose, CA +1

Sr. Applied Scientist at Adobe focused on improving the quality and controllability of generative multimodal models, specifically in mid-training capabilities for image and video editing. The role involves designing and implementing training pipelines, identifying quality gaps, developing data curation and distributed training workflows, and optimizing inference for production environments.

What you'd actually do

  1. Design and implement end-to-end training pipelines to build and improve editing quality for both images and videos.
  2. Lead core development for specific mid-training areas (e.g., image to image and image to video editing), while aligning with broader team strategy.
  3. Identify quality gaps in generative models and propose targeted mid-training solutions.
  4. Develop scalable workflows for data curation, data quality improvements, and distributed training.
  5. Increase engineering velocity and reducing iteration cost by systematizing mid-training experimentation and deployment.

Skills

Required

  • Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related field
  • Proven track record in mid-training or continual pre-training of large-scale multimodal models, specifically on cross modality for image and video data
  • Deep understanding of pre-training, mid-training and/or post training for multimodal generative models
  • Deep understanding of modern diffusion-based architectures (DiT) and conditional generation and editing, e.g., instruct editing, to drive precise structural and temporal control in production environments
  • 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
  • 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

Nice to have

  • publications experience
  • previous industry level intern experience

What the JD emphasized

  • mid-training
  • editing quality
  • multimodal models
  • image and video data
  • conditional generation and editing
  • Vision-Language Models (VLMs)
  • model inference and deployment

Other signals

  • multimodal models
  • generative AI
  • mid-training
  • editing quality