We are seeking a research scientist to precisely improve Gemini's information-seeking capabilities. The successful candidate will work on post-training research in Gemini, focusing on quality of information-seeking responses. This role offers an opportunity to explore fundamental issues in modelling and data interventions for information-seeking scenarios, with significant opportunities in shaping Google's products in this space.
Responsibilities:
- Conduct research on post-training methods for information-seeking scenarios in Gemini, including reinforcement learning and self-supervised training.
- Develop novel evaluation methods for improving model quality, grounding, and factuality.
- Investigate orchestration of tool calls and improved retrieval methods for information-seeking scenarios.
Requirements:
- PhD in a relevant area, or an equivalent research/publication record.
- Strong software-engineering skills in addition to a research background.
Preferred Qualifications:
- Experience in reinforcement learning.
- Experience in post-training methods.
- Experience in Large Language Models for information-seeking scenarios.
The US base salary range for this full-time position is between $147,000 USD – 211,000 + bonus + equity + benefits.
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