Applied Scientist

Adobe; San Jose (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Adobe seeks Applied Scientists and Engineers to shape generative AI data platforms and evaluation systems for next-generation image and video models. You will build metadata-driven taxonomies, data quality dashboards, and robust evaluation frameworks to improve dataset diversity and model performance.

Collaborate with research, engineering, and product teams, implement production-ready solutions in Python and PyTorch, and translate research into scalable production systems.

Qualifications

• Master's or Ph.D. in Computer Science, ML, AI, or related field.
• Experience building scalable ML infrastructure and synthetic data platforms.
• Strong knowledge of diffusion models, transformers, and multimodal AI workflows.
• Proficient in Python, PyTorch, data engineering, metadata management, and dashboards.
• Ability to translate research into production systems; publications a plus.

Responsibilities

• Design scalable synthetic data generation platforms for large-scale training.
• Build metadata-driven taxonomies and dashboards to monitor distribution and quality.
• Develop robust evaluation pipelines across accuracy, diversity, and safety metrics.
• Collaborate with researchers, engineers, and product teams to productionize AI features.
• Implement production-ready ML solutions using Python, PyTorch, and modern ML infra.

Skills

Python

PyTorch

Data engineering

Metadata management

Dashboard development

Evaluation frameworks

Communication skills

Education

Master's or Ph.D. in Computer Science or ML/AI

Job description

Changing the world through digital experiences is what Adobe is all about. Adobe Firefly is redefining creativity through generative AI, enabling millions of users to create and transform images, videos, and digital experiences. The Adobe Firefly ASML team is seeking Applied Scientists and Engineers passionate about building scalable generative AI data platforms, synthetic data pipelines, and evaluation systems for next-generation image and video models. This role focuses on developing metadata-driven taxonomies, data quality dashboards, and robust evaluation frameworks to improve dataset diversity, quality, and model performance.

Job Responsibilities

• Design and develop scalable synthetic data generation platforms and optimize image/video data pipelines for large-scale generative AI training.
• Build metadata-driven taxonomies, dashboards, and analytics tools to monitor dataset distribution, diversity, sample quality, and training readiness.
• Develop robust evaluation pipelines to assess data and model quality across accuracy, diversity, robustness, efficiency, and safety metrics.
• Collaborate with research scientists, engineers, and product teams to translate multimodal AI innovations into scalable production systems.
• Implement production-ready solutions using Python, PyTorch, and modern ML infrastructure to support generative AI workflows and continuous data quality improvement.

What You'll Need to Succeed

• Master's or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
• Experience building scalable ML infrastructure, synthetic data platforms, or multimodal image/video data pipelines.
• Strong knowledge of diffusion models, transformers, foundation models, and multimodal generative AI workflows.
• Proficiency in Python, PyTorch, data engineering, metadata management, dashboard development, and evaluation frameworks.
• Experience developing data quality, taxonomy, and visualization solutions for large-scale AI datasets.
• Excellent communication and collaboration skills with cross-functional research, engineering, and product teams.
• Demonstrated ability to translate research into production systems; publications in AI/ML or Computer Vision are a plus.

Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic.