Principal Applied Researcher
Adobe · San Jose, CA · Engineering and Product
About this role
Adobe is hiring a senior-level Research Scientist in the machine learning function based in San Jose, CA. The posting calls out experience with Express, TensorFlow, PyTorch, LLMs. Compensation is listed at $206,300–$388,000 per year.
- Role
- Research Scientist
- Function
- machine learning
- Level
- senior
- Track
- Individual contributor
- Employment
- Full-time
- Location
- San Jose, CA
- Department
- Engineering and Product
- Posted
- May 13, 2026
More roles at Adobe
Job description
from Adobe careersApplied Researcher, AI Foundations- Adobe Express
The Opportunity
Adobe Express empowers users to seamlessly construct striking designs, whether they are novices or experienced engineers, on a user-friendly platform improved by groundbreaking AI capabilities. The AI Foundations team sits at the core of this vision, driving innovation to make Adobe Express smarter, faster, and more adaptive! By developing modular, reusable AI systems and crafting a robust Horizon AI Stack, this team will play a pivotal role in transforming creative workflows. Joining this team means crafting the future of creativity, solving complex engineering challenges, and delivering scalable AI solutions that redefine what is possible in design!
What You'll Do
- Build innovative machine learning models that drive Agentic and other Generative AI scenarios for Adobe Express.
- Develop and deploy advanced ML models in areas such as computer vision, NLP, and multimodal learning.
- Contribute to foundational AI systems for intelligent assistance, layout automation, image generation, and motion storytelling.
- Design and build modular ML components that integrate into Adobe’s Horizon AI Stack and serve multiple creative workflows.
- Drive the end-to-end ML lifecycle—from experimentation to evaluation, optimization, and production deployment.
- Collaborate closely with Adobe Research, engineering, and product teams to bring AI-powered features to life.
- Design evaluation strategies using a combination of automated metrics, LLM based judges, and user feedback.