Applied Scientist

Adobe
2 Locations$165k – $239kPosted 25 April 2026

Job Description

The Opportunity Join Adobe Firefly’s Applied Science & Machine Learning (ASML) group and help advance AI-powered content generation. In this role, you’ll work closely with a collaborative team to refine and compress large-scale AI models. Your work will impact millions of users. Our mission is to build inclusive, high-performing teams and create an environment where new ideas can come from anyone—including you. The Adobe Firefly Applied Science & Machine Learning (ASML) group is looking for an Applied Scientist / Machine Learning Engineer with experience in post-training and distillation of large generative AI models. In this role, you will help improve the quality, efficiency, and scalability of generative models for images and video. You’ll partner with researchers and engineers to build and refine post-training pipelines, including supervised fine-tuning (SFT), preference optimization, and model distillation. Your work will help adapt large, complex models into efficient production systems. As part of this team, you’ll contribute to the performance and scalability of Firefly’s generative AI systems and help deliver new creative capabilities to millions of users. You’ll collaborate across data, modeling, and infrastructure teams to bring post-training improvements into production. Job Responsibilities Develop and run distillation pipelines to transfer capabilities from large teacher models to efficient student models Apply and improve post-training methods, including supervised fine-tuning (SFT), preference optimization (DPO/GRPO), and reward-based learning Build tools and infrastructure for teacher rollout generation, data pipelines, and training workflows Design and run experiments to improve model quality, efficiency, and instruction alignment Partner with research scientists to translate ideas into scalable pipelines and production-ready systems Evaluate models using automated metrics and human feedback to guide improvements Optimize models for deployment, including compression and inference performance Collaborate with cross-functional teams (data, research, and product) to integrate improvements into Adobe Firefly What You Need to Succeed MS or PhD in Computer Science or a related field Experience with machine learning algorithms and model distillation techniques Strong programming skills in Python or similar languages Experience training and optimizing AI models Preferred Qualifications Experience with large-scale data pipelines Familiarity with Adobe products #FireflyGenAI About Adobe Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity. Our 30,000 employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours. Let’s Adobe together At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our values and culture , focus on people, purpose and community , Adobe for All , comprehensive benefits programs , the stories we tell , the customers we serve, and how you can help us advance our mission of empowering everyone to create. 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, vete ... (truncated, view full listing at source)
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