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Research Scientist, Gemini Factuality, DeepMind
GoogleMountain View, CA, USA2+ years
Apply Minimum qualifications:
- PhD in Computer Science, a related field, or equivalent practical experience.
- 2 years of experience leading a research agenda.
Preferred qualifications:
- 2 years of coding experience.
- 1 year of experience leading research efforts and influencing other researchers.
About the job
As a Research Scientist, you will be responsible for improving the factuality of the Gemini model family. You will bridge the gap between research and production. You will also design advanced training methodologies to reduce hallucinations and build automated evaluation systems to measure the model's accuracy across real-world knowledge domains.
Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, 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: $174000 - $253000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Develop evaluation methods for detecting factual inaccuracies in all LLM generated content.
- Design and implement advanced pre-training, supervised fine tuning (SFT), and reinforcement learning strategies to maximize the generation of factual content.
- Innovate on algorithmic interventions and data curation strategies to improve model calibration, ensuring Gemini accurately identifies its own knowledge boundaries.
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