Director, Multimodal Data Research
Adobe · San Jose, CA · Engineering and Product
About this role
Adobe is hiring a director-level Research Scientist in the machine learning function based in San Jose, CA. The posting calls out experience with Express, ETL, Machine Learning, Performance Optimization and roughly 10+ years of relevant work. Compensation is listed at $192,700–$392,500 per year.
- Role
- Research Scientist
- Function
- machine learning
- Level
- director
- Track
- Individual contributor
- Employment
- Full-time
- Location
- San Jose, CA
- Experience
- 10+ years
- Department
- Engineering and Product
- Posted
- Mar 16, 2026
More roles at Adobe
Job description
from Adobe careersWe are seeking a Director (M60) to lead the Multimodal Data Research organization within Applied Science and Machine Learning (ASML). This organization is responsible for scaling, quality, and innovation in multimodal training data—spanning image, video, and audio—that powers Adobe Firefly’s foundational generative and editing models.
The Multimodal Data Research organization sets the data foundation for Firefly’s multimodal intelligence, owning first‑party strategy and execution across ingestion, filtering, captioning, generation and editing‑centric datasets, data‑driven training experiments, and next‑generation data frameworks. By grounding model training in high‑quality, diverse, and workflow‑authentic data, the team directly shapes Firefly’s creative capabilities. This role calls for a technically deep director who can articulate long‑term vision, lead multiple senior teams, and orchestrate execution across a fast‑moving multimodal AI ecosystem.
What You’ll Do
Organizational Leadership & Strategy
- Set and own the long‑term vision and roadmap for multimodal data research across image, video, and audio.
- Define clear investment priorities across data quality, coverage, scalability, and iteration speed to support Firefly’s foundational and editing‑centric models.
- Build and evolve an organization that balances research innovation with operational excellence in data delivery.
- Lead multiple teams spanning multimodal data quality and scaling, captioning and prompt rewriting, data frameworks and infrastructure, and systematic training ablations to advance large‑scale pretraining and editing datasets.