Principal Research Scientist – Scaling
Databricks - San Francisco, California
Posted Apr 23, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
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- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- 401(k) match
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Schedule
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- Weekend work
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About this role
Principal Research Scientist – Scaling San Francisco, California Principal Research Scientist - Scaling P-1227 About Databricks AI At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems, from security threat detection to cancer drug development, by building and running the world's best data and AI platform. The Databricks AI Research organization enables companies to develop AI models and agents using their own data, with technologies ranging from post-training open source LLMs to developing advanced multi-agent architectures. Databricks AI is committed to the belief that a company's AI models and agents are just as valuable as any other core IP, and that high-quality AI should be available to all. About the Scaling Research Team The Databricks AI Scaling team focuses on pushing the boundaries of large language model (LLM) training and inference efficiency beyond what is required to support existing models. The team explores novel avenues for scaling and efficiency improvements across algorithms, systems, and infrastructure, requiring researchers who can both drive independent research agendas and dive deep into low‑level implementation details with engineering partners. Role Summary As a Principal Research Scientist - Scaling, you will lead a team of world‑class researchers and engineers to advance the state of the art in large‑scale machine learning, focusing on post-training, RL and inference efficiency, optimization, and scaling. You will define and execute a research roadmap that advances the Databricks AI platform and delivers tangible improvements to how customers train, serve, and adapt LLMs at scale, working closely with
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