Adobe
- HQ
- San Jose, US
- Founded
- 1982
- Size
- 25,000+
- Website
- adobe.com
Currently tracking 206 active AI roles, with 444 new openings in the last 4 weeks. Primary focus: Agent · Engineering. Salary range $114k–$397k (avg $224k).
| Title | Stage | AI score |
|---|---|---|
| Senior Machine Learning Engineer - Firefly Senior Machine Learning Engineer focused on building production pipelines and systems to improve deployed generative AI models using user feedback and behavioral signals. The role involves preference modeling, data processing, and evaluation infrastructure for multimodal generative workflows. | Post-trainServe | 9 |
| Senior Staff Applied Scientist - AI/ML Senior Staff Applied Scientist role at Adobe focusing on transforming AI/ML research breakthroughs into innovative product features for Generative AI, LLMs, and multimodal AI. The role involves scouting, adapting, and improvising research, prototyping, rapid experimentation, and deploying practical innovations for Adobe's products. Responsibilities include developing and enhancing GPU-accelerated pipelines for model training and inference, collaborating with researchers and ML engineers, and publishing work. Requires a Ph.D. or Masters with 10+ years of experience in AI/ML, with expertise in training AI/ML models in multimodal LLMs, Image, or Video, proficiency in training and optimizing large-scale models, and experience with post-training techniques like fine-tuning and alignment. |
| Post-trainServe |
| 9 |
| Senior Machine Learning Engineer Senior Machine Learning Engineer at Adobe Journey Optimizer B2B, focusing on AI-powered customer journey orchestration. The role involves training and fine-tuning ML models, architecting ML pipelines, and contributing to the deployment and production operations of ML models and systems. Experience with generative AI, LLMs, fine-tuning, and MLOps is required, with a preference for RAG, agentic workflows, and personalization use cases. | Post-trainAgent | 8 |
| Sr. Applied Scientist Sr. Applied Scientist at Adobe focused on improving the quality and controllability of generative multimodal models, specifically in mid-training capabilities for image and video editing. The role involves designing and implementing training pipelines, identifying quality gaps, developing data curation and distributed training workflows, and optimizing inference for production environments. | Post-trainData | 8 |
| Sr. Applied Scientist This role focuses on developing and implementing ML models for search, recommendations, and generative AI use cases within Adobe's creative suite. It involves fine-tuning LLMs and Diffusion models, optimizing inference, and processing multimodal data to assist users and recommend assets. The role also includes research into novel LLM architectures and evaluation methods. | Post-trainAgent | 8 |
| Applied Scientist Applied Scientist at Adobe focused on transforming research breakthroughs in Generative AI, LLMs, and multimodal AI into innovative product features for millions of users. The role involves scouting, adapting, and improvising the latest research, prototyping, and deploying practical innovations, with a strong emphasis on training and optimizing large-scale models, including post-training techniques and GPU-accelerated pipelines for both training and inference. | Post-trainServe | 8 |
| Applied Scientist 3 This role focuses on post-training and distillation of large generative AI models for images and videos, aiming to improve quality, efficiency, and deployability. Responsibilities include developing distillation pipelines, refining post-training methods (SFT, DPO, GRPO, reward-based learning), building infrastructure for training workflows, and optimizing models for deployment efficiency. The role collaborates with researchers and engineers to adapt complex models into efficient production versions. | Post-trainServe | 7 |