Research Scientist, Gemini Multilinguality, Google DeepMind

Google place Mountain View, CA, USA

• Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, or a related technical field, or equivalent practical experience.
• Experience with research in one or more of the following areas: LLM post-training and alignment, reinforcement learning, multilingual benchmarking, reasoning and agentic systems, or foundation model training.
• Experience training, post-training, or evaluating foundation models.

• Ability to translate open-ended research ideas into landed improvements in frontier models across multi-team collaborations.
• Excellent communication and collaboration skills, and a genuine passion for human languages and building AI that works for people everywhere.

Artificial intelligence will be one of humanity's most transformative inventions. At Google Deep Mind, 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.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

• Drive an ambitious research agenda in post-training, reinforcement learning, and agentic modeling to improve Gemini's reasoning, tool use, factuality and multimodal performance across global languages, dialects, and locales.
• Develop scalable algorithms for cross-lingual capability transfer, synthetic data generation, reward modeling, and self-improvement flywheels that close the quality gap for both major global languages and low-resource languages.
• Improve local and cultural grounding, factuality, naturalness, and language consistency across long-horizon multi-turn and multimodal interactions (text, speech, image, and video).
• Design trustworthy evaluation methodologies, evaluations, and human-grounded reward signals to measure and steer complex agentic and culturally nuanced model behaviors.
• Partner closely with core Gemini post-training, agentic, and pretraining/mid-training teams- as well as product partners across Google to land research breakthroughs directly into flagship Gemini models.

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