Research Scientist, Multilingual, Multicultural and Multimodal LLM, DeepMind
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Key details
- Location
- Tokyo, Japan
- Work style
- Onsite
- Level
- Mid level
- Experience
- 2–5 yrs
- Function
- Data & ML
- Focus area
- AI / ML
- Posted
- 1w ago
Skills
About the role
About the job
We are seeking highly motivated and innovative Research Scientists to join our team in Tokyo, focused on advancing the multilingual, multicultural, and multimodal large language models (LLMs).
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.
Responsibilities
- Design, implement, and evaluate multilingual, multicultural, and multimodal capabilities in Gemini and other frontier models at Google.
- Work closely with other research scientists, engineers, and product teams across Google DeepMind, fostering a collaborative and intellectually stimulating environment.
- Contribute to Gemini and other frontier models at Google, with applications across various domains, directly influencing the future of Google products and services.
- Contribute to the research community by sharing insights and participating in external academic workshops and conferences.
Minimum qualifications:
- PhD in Computer Science, a related field, or equivalent practical experience.
- 2 years of experience in coding.
- Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.
- One or more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
Preferred qualifications:
- 1 year of experience owning and initiating research agendas.
- Experience with multilingual, multicultural, or multimodal learning in LLMs.
- Experience with deep learning, natural language processing, computer vision, or speech processing.
- Experience with pretraining, post-training techniques, prompt engineering, few-shot learning, and evaluations.
- Familiarity with large-scale model training and deployment.
- PhD in Computer Science, a related field, or equivalent practical experience.
- 2 years of experience in coding.
- Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.
- One or more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
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