Responsibilities Research and develop novel AI-based solutions for image and video understanding, generation, transformation, and optimization. Explore and evaluate the latest advances in Generative AI and multimodal models, and identify practical applications for Cloudinary products. Train, fine-tune, evaluate, and optimize large-scale AI models and pipelines. Design scalable approaches for serving AI models efficiently in production environments handling billions of assets. Prototype new ideas rapidly and collaborate with engineering teams to bring research innovations into production. Investigate opportunities to create proprietary models, unique workflows, and differentiated AI capabilities. Stay up to date with the rapidly evolving AI ecosystem, including open-source and commercial foundation models. Collaborate across research, engineering, and product teams to define future AI-driven experiences and capabilities. Help shape the technical direction of Cloudinary’s AI initiatives. Requirements MSc or PhD in Computer Science, Electrical Engineering, Mathematics, Physics, or a related field - or equivalent industry experience. 5+ years of experience in software development and/or applied AI research. Strong hands-on experience with modern AI and Generative AI technologies. Experience training, fine-tuning, evaluating, and deploying AI models. Strong Python programming skills. Experience with modern AI frameworks and ecosystems such as PyTorch, Diffusers, Transformers, or similar. Experience working with large-scale distributed cloud environments. Ability to take research ideas from concept through experimentation and into production. Strong problem-solving and analytical thinking. Excellent communication and collaboration skills. Nice to Have Background in computer vision, image processing, or video technologies. Experience with multimodal AI models and media generation workflows. Familiarity with optimization and inference acceleration techniques. Experience building scalable AI infrastructure or model-serving systems. Contributions to research publications, open-source projects, or AI communities. Experience working in production environments serving large-scale applications.
