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Snapshot
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.
About Us
Our team aims to build and improve state of the art generative models for images, videos and other modalities. We are interested in candidates familiar with the latest generative modelling techniques such as dvariational auto-encoders,iffusion models, generative adversarial models, etc. You will need to be able to understand and work on the promising research directions, and implement them at scale. Therefore, both a strong theoretical background and a hand-on experience with large-scale machine learning is required. You will collaborate closely with other leading researchers, contributing to the development of cutting edge generative models and translating research into products across Google and externally.
The Role
We’re looking for a Research Engineer with exceptional engineering skills and understanding of large scale data processing, as well as a strong working knowledge of machine learning experimentation.
Key responsibilities:
- Develop, maintain and improve large scale data pipelines that generate Gemini’s pre-training data which is critical to the quality and capabilities of Gemini models.
- Conduct careful empirical research to validate novel datasets and novel data processing techniques.
- Collaborate with team members to develop scaling laws and understanding of how large scale training and large scale data interact.
- Collaborate with the wider Gemini team, enganging closely with the Model, Infrastructure and the Post-Training teams.
About You
In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:
- A proven track record of working with large scale data processing pipelines
- A proven track record of empirical experimentation such as work in deep learning of empirical sciences.
- A strong background in large scale engineering, understanding of distributed systems.
In addition, the following would be an advantage:
- A degree or PhD in machine learning or closely related field, or similar experience
- Experience with Large Language model training
- Experience with working on deep learning, especially on creating deep learning datasets.
