Key responsibilities include:

• Post-training/instruction tuning state-of-the-art language models, focusing on text-to-text, image/video/audio-to-text modalities and agentic capabilities
• Exploring data, reasoning, and algorithmic solutions to ensure Gemini models are safe and work for everyone
• Improving Gemini's adversarial robustness, with a focus on high-stakes abuse risks
• Designing and maintaining high-quality evaluation protocols to assess model behaviour gaps and headroom related to safety and fairness
• Developing and executing experimental plans to address known gaps or construct entirely new capabilities

To succeed in this role, you should have a PhD in Computer Science or a related field, significant LLM post-training experience, and a track record of publications at top conferences. Experience in reward modelling and reinforcement learning for LLMs instruction tuning, long-range reinforcement learning, safety, fairness, and alignment is an advantage.

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