Research Scientist, Gemini Safety
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What the team is looking for.
We're seeking a versatile Research Scientist to join our Gemini Safety team. As a Research Scientist, you will apply and develop data and algorithmic cutting-edge solutions to advance our latest user-facing models. Your work will focus on advancing the safety and fairness behavior of state-of-the-art AI models, driving the development of foundational technology adopted by numerous product areas, including Gemini App, Cloud API, and Search.
Key responsibilities include:
- Post-training/instruction tuning state-of-the-art LLMs, 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, maximally helpful, and work for everyone
- Improve Gemini's adversarial robustness, with a focus on high-stakes abuse risks
- Design and maintain high-quality evaluation protocols to assess model behavior gaps and headroom related to safety and fairness
- Develop and execute experimental plans to address known gaps, or construct entirely new capabilities
- Drive innovation and enhance understanding of Supervised Fine Tuning and Reinforcement Learning fine-tuning at scale
To succeed as a Research Scientist in the Gemini Safety team, we look for the following skills and experience:
- PhD in Computer Science, a related field, or equivalent practical experience
- Significant LLM post-training experience
- Experience in Reward modeling and Reinforcement Learning for LLMs Instruction tuning
- Experience with Long-range Reinforcement learning
- Experience in areas such as Safety, Fairness, and Alignment
- Track record of publications at NeurIPS, ICLR, ICML
- Experience taking research from concept to product
- Experience with collaborating or leading an applied research project
- Strong experimental taste: Good judgment regarding baselines, ablations, and what is worth testing
- Experience with JAX
- PhD in Computer Science
- LLM post-training experience
- Reward modeling and Reinforcement Learning for LLMs Instruction tuning
- Long-range Reinforcement learning
- Safety, Fairness, and Alignment
- NeurIPS, ICLR, ICML publications
- Research from concept to product
- Collaborating or leading an applied research project
- JAX
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