Senior Applied Scientist

Adobe•San Jose, CA

19h•$142,700 - $270,950•Remote

About The Position

Adobe Firefly’s Applied Science & Machine Learning (ASML) group invites an Applied Scientist / Machine Learning Engineer passionate about post-training and distillation of large generative AI models to join the team. This role will focus on raising the quality, efficiency, and deployability of Adobe’s generative models for images and videos. The chosen candidate will collaborate with researchers and engineers to build and refine post-training pipelines such as supervised fine-tuning (SFT), preference optimization, and model distillation. These efforts will facilitate adapting large complex models into efficient production versions. This role influences the performance and scalability of Firefly’s generative AI systems, facilitating next-generation creative functionalities for millions of users. As an Applied Scientist at Adobe, you will join a distinguished team of applied researchers and engineers committed to developing and improving generative AI systems. You will work alongside data, modeling, and infrastructure groups to implement post-training upgrades into production systems that support Adobe products.

Requirements

Expertise in machine learning algorithms and model distillation techniques
Strong programming skills in Python or similar languages
Experience with AI model training and optimization

Nice To Haves

Advanced degree or relevant experience in a related field
Background in large-scale data pipelines
Knowledge of Adobe products

Responsibilities

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.