Research Scientist: Multilingual, Multicultural and Multimodal LLM

We are seeking highly motivated and innovative Research Scientists to join our team in Tokyo, focused on advancing the state-of-the-art in multilingual, multicultural, and multimodal large language models (LLMs).

As a Research Scientist at Google DeepMind, you will:

• Develop and evaluate multilingual, multicultural, and multimodal LLMs: Design, implement, and evaluate multilingual, multicultural, and multimodal capabilities in Gemini and other frontier models at Google.
• Collaborate with a world-class team: Work closely with other research scientists, engineers, and product teams across Google DeepMind, fostering a collaborative and intellectually stimulating environment.
• Contribute to real-world impact: See your work contribute to Gemini and other frontier models at Google, with applications across various domains, directly influencing the future of Google products and services.

About you

• You are a passionate and talented researcher with a strong foundation and a proven ability to conduct impactful research in AI.
• You embrace change and thrive under ambiguity.
• You have a collaborative mindset and are excited to work as part of a team to tackle ambitious research challenges.
• You are passionate about seeing your research translated into real-world products that improve the lives of users and are eager to work in an environment where research has a direct path to product impact.

Requirements

• Ph.D. (or equivalent research experience) in Computer Science, Artificial Intelligence, or a related field.
• Strong publication record in top-tier AI conferences or journals.
• Solid understanding of deep learning, natural language processing, computer vision, and/or speech processing.
• Experience with relevant ML frameworks such as JAX, TensorFlow, or PyTorch.
• Excellent communication and collaboration skills.

In addition, the following would be an advantage:

• Experience with multilingual, multicultural, and/or multimodal learning in LLMs.
• Experience with pretraining, post-training techniques, prompt engineering, few-shot learning, and evaluations.
• Familiarity with large-scale model training and deployment.
• Strong programming skills in Python or similar languages.