Google DeepMind
Research Scientist, Reasoning
Full-time
On-site, Mountain View, CA
5 days ago
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About
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
This role's goal is to help build an AI scientist. We are not focused on specific scientific applications, but rather targeting a generalist AI scientist capable of solving the hard reasoning challenges on the path to AGI. We integrate with the Gemini core effort – identifying promising directions to improve model capabilities overall and reasoning in particular.
The Role
This role touches all aspects of the large language model pipeline. Work may include model training, data pipeline development, evaluation, finetuning and RL training as well as tooling and support. A candidate should be comfortable both with running large scale workflows as well as understanding how they fit into the larger team projects.
Key responsibilities:
- Design and implement new features and tools within Google DeepMind's machine learning frameworks to enable and accelerate research efforts.
- Collaborate with researchers within Google DeepMind in experiment design and execution with large language models.
- Design, implement, analyze and optimize large-scale machine learning workflows.
About You
In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience:
- Distributed Systems
- Degree in Computer Science or Physics or related quantitative field with 5+ years of relevant experience.
- Self-directed engineer who can drive new research ideas from conception, experimentation, to productionization in a rapidly shifting landscape.
- Experience with large-scale dataflow pipelines.
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