Develop and run distillation pipelines to transfer capabilities from large teacher models into smaller, efficient student models.
Carry out and refine post-training methods including supervised fine-tuning (SFT), preference optimization (DPO/GRPO), and reward-based learning.
Build infrastructure and tools for teacher rollout creation, distillation data pipelines, and training workflows.
Carry out experiments aimed at improving model quality, efficiency, and instruction alignment for generative AI models.
Collaborate closely with research scientists to convert research ideas into scalable training pipelines and production-ready implementations.
Evaluate models using both automated metrics and human preference signals to guide post-training improvements.
Optimize models for deployment efficiency, including distillation, model compression, and inference performance.
Collaborate with various groups such as data, research, and product units to incorporate post-training improvements into Adobe Firefly systems.

Expertise in machine learning algorithms and model distillation techniques
Strong programming skills in Python or similar languages
Experience with AI model training and optimization
Advanced degree or relevant experience in a related field, background in large-scale data pipelines, and knowledge of Adobe products.

Senior Applied Scientist

Key skills