Minimum qualifications: PhD in Computer Science, a related field, or equivalent practical experience. Experience of advanced deep learning, with a specific experience in foundational or practical contributions to diffusion models. Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch. Publication record in machine learning conferences or journals (e.g., NeurIPS, ICML, ICLR, KDD, AAAI). Preferred qualifications: Passionate for AI and a collaborative, open-minded approach to problem-solving. About the job Our mission is to design and develop novel generative methodologies from the ground up, deploying them to solve some of the most complex challenges across two distinct domains: media models and groundbreaking scientific discovery. In this role, you will actively collaborate with our colleagues in Amsterdam, which will include regular on-site visits. Serving as a critical bridge between core machine learning research and applied domains, you will utilize advanced diffusion techniques and novel generative methods to address ambitious problems in both media synthesis and science. Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority. We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Responsibilities Design, deploy, and accelerate generative models leveraging techniques like distillation to solve specific, high-complexity challenges. Maintain a flexible, problem-focused approach to research, identifying and utilizing the most effective algorithmic tools to overcome concrete technical roadblocks. Evaluate the datasets, training methodologies, and final outputs required to push the performance of large-scale, domain-specific generative models. Report developments and share deep scientific insights efficiently both verbally and in writing, mentoring peers and elevating the team's overall technical capability.