Senior Applied Scientist

Adobe is dedicated to changing the world through digital experiences. We offer everything needed to build and deliver outstanding experiences to everyone—from emerging artists to global brands. We are passionate about enabling people to develop beautiful images, videos, and apps. We also help companies change how they connect with customers across all screens. Our mission is to hire top talent and build outstanding employee experiences with respect and equal opportunity for all. New ideas can come from anywhere in our organization, including from you. 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, [... source excerpt omitted ...] d 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. Job 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 [... source excerpt omitted ...] ality, 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. What you need to succeed Expertise in machine learning algorithms and model distillation techniques St