Sr. Applied Scientist

Adobe • San Jose, CA

21h • $164,000 - $313,300

About The Position

About the Role: This role targets excelling talent needed for key product areas, requiring significant domain expertise to accelerate critical initiatives . You will be expected to materially improve the quality and controllability of Adobe’s generative multimodal models . This involves strengthening Adobe’s competitive position in generative AI quality and alignment through sustained improvements in mid-training capabilities .

Requirements

• Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related field preferred.
• 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.
• Since this is junior role, we need the role has strong publications experience and previous industry level intern experience

Responsibilities

• Design and implement end-to-end training pipelines to build and improve editing quality for both images and videos.
• Lead core development for specific mid-training areas (e.g., image to image and image to video editing), while aligning with broader team strategy.
• Identify quality gaps in generative models and propose targeted mid-training solutions.
• Develop scalable workflows for data curation, data quality improvements, and distributed training.
• Increase engineering velocity and reducing iteration cost by systematizing mid-training experimentation and deployment.
• Partner closely with research, data, evaluation, infrastructure, pre-training and post-training teams to push the editing quality for both images and videos.
• Mentor junior engineers and contribute to raising the technical bar of the mid-training team.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees