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At Google DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. We are seeking a highly motivated Research Scientist to join our team and contribute to groundbreaking fundamental research and deployment in large scale pre-training.

Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.

We’re looking for a Research Scientist with a strong empirical and theoretical understanding of deep learning with a focus on data, as well as strong engineering skills and understanding of distributed systems.

• Conduct careful empirical research to validate novel research ideas to improve performance of Gemini models.
• Develop strong intuitions grounded in data scaling laws and theoretical insights that can lead to research breakthroughs and new model capabilities.
• Dive deep into specific areas of the pre-training stack to improve our understanding of large scale training dynamics.
• Collaborate with the wider Gemini team, engaging closely with the Model, Infrastructure and the Post-Training teams.

In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience:

• A proven track record of large scale deep learning research with hands-on experience with Python and neural network training (publications, open-source projects, relevant work experience, …).
• An in-depth knowledge of large scale training dynamics.
• Ability to communicate technical ideas effectively, e.g. through discussions, whiteboard sessions, written documentation.

In addition, the following would be an advantage:

• Experience with large scale data processing pipelines.
• A PhD in machine learning or closely related field, or similar experience.
• Experience with distributed systems and large scale deep learning performance optimisation.
• Experience with running large scale data processing pipelines.