About the job
At Google DeepMind, we believe that Artificial Intelligence is one of the most transformative inventions for humanity. Our team comprises scientists, engineers, and machine learning specialists dedicated to advancing cutting-edge AI technologies for the benefit of society and scientific progress, with a strong emphasis on safety and ethical considerations.
The Gemini Safety team is responsible for ensuring the safety and fairness of our latest Gemini models. We are seeking a Research Scientist who will leverage data and innovative algorithms to enhance the performance of our user-oriented models. This role demands a fast-paced, highly collaborative work environment where support and teamwork are paramount.
About Us
Google DeepMind is at the forefront of AI innovation, focusing on using our technologies for meaningful public impact and exploring scientific frontiers. We work closely with various partners to tackle significant challenges while prioritizing ethical standards and safety protocols.
Position Overview
We are in search of a dynamic Research Scientist who excels in both exploring novel research inquiries and implementing technical solutions. Our team's mission is to enhance the safety and fairness of state-of-the-art AI models, contributing foundational technology to multiple product areas including Gemini App, Cloud API, and Search.
Key Responsibilities:
- Optimize post-training and instruction tuning of state-of-the-art LLMs across various modalities including text-to-text and image/video/audio-to-text.
- Investigate data-driven, reasoning, and algorithmic strategies to ensure Gemini Models remain safe, highly effective, and accessible to all users.
- Enhance Gemini’s resilience against adversarial threats, particularly concerning high-risk abuse scenarios.
- Develop and uphold high-quality evaluation protocols to identify model behavior gaps and opportunities related to safety and fairness.
- Create and implement experimental plans to bridge identified gaps or develop new capabilities.
- Foster innovation and deepen our understanding of Supervised Fine Tuning and Reinforcement Learning fine-tuning at scale.

