Research Scientist, World Models, DeepMind

Minimum qualifications:

PhD in Computer Science, Machine Learning, Statistics, Mathematics, a related technical field, or equivalent practical experience.

2 years of experience publishing research in machine learning conferences or releasing open/closed-source software models.

2 years of experience in diffusion models, multimodal generative models, or generative video models.

Preferred qualifications:

3 years of experience working in frontier AI research labs on pre-training or post-training teams.

Experience writing TPU/GPU kernels (e.g., JAX, CUDA) to optimize model performance and real-time inference.

Experience designing, training, and scaling generative pixel or real-time video architectures.

Track record of cross-functional research collaboration delivering models.

About the job

We are looking for a Research Scientist to develop multimodal generative models and advance the frontier of world modeling research. This role focuses on applying diffusion models to real-time video generation, joining our core modeling team to build capable, interactive models.

In this role, you will collaborate closely with researchers and engineers to set up tight feedback loops across modeling and data efforts. You will be involved in significant research, road-mapping, and engineering work to deliver world models.

When assessing technical background, we take a holistic view of the mix of scientific, machine learning, and systems experience. We do not expect applicants to be experts in all fields simultaneously.

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

Drive research and implementation of new architectures to build frontier generative models with real-time performance.

Design, evaluate, and generate datasets to discover new model capabilities.

Collaborate across capability workstreams to discover and scale optimal training recipes.

Work across the training stack to land new generative and world model capabilities.

Stay current with the latest research literature and advancements in diffusion models and generative video architectures.