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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 (architecture, optimisation, data, LLMs), as well as strong engineering skills and understanding of distributed systems.

• Develop strong intuitions grounded in scaling laws and theoretical insights that can lead to research breakthroughs and new model capabilities.
• Understand and measure effects of scaling on training dynamics and model performance via scaling laws and other analysis tools. 
• Conduct modelling research: Use empirical and theoretical insights to derive novel research ideas that improve Gemini models. 
• Dive deep into specific aspects of pre-training (modelling, optimisation, data) to understand and improve model dynamics.
• Collaborate with the wider Gemini team, engaging closely with the Data, 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 with hands-on experience with Python and neural network training (publications, open-source projects, relevant work experience, …).
• An in-depth knowledge of Transformer models and LLM training dynamics.
• Ability to communicate technical ideas effectively, e.g. through discussions, whiteboard sessions, written documentation.

In addition, the following would be an advantage: 

• A PhD in machine learning or closely related field, or similar experience.
• Experience with GPU/TPU kernel development (Triton, Pallas).
• Experience with distributed systems and large scale deep learning performance optimisation.
• Experience with running large scale data processing pipelines.