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PhD GenAI Research Scientist Intern

Databricks - San Francisco, California

Posted Nov 7, 2023

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
  • Fertility benefits: Not verified
  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
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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Verification
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Salary
$54-$60/hr From the posting source checked Jun 20, 2026
401(k) match
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Market context

U.S. role benchmark (BLS OEWS)
$111,944 U.S. median for this role
Projected growth (BLS Employment Projections)
+13.7% - Much faster than average

6% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function
Data From the posting source checked Jun 20, 2026
Seniority
Entry From the posting source checked Jun 20, 2026

Schedule

Shift type
Not verified
Weekend work
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Company

Company stage
Growth-stage From the posting source checked Jun 20, 2026
Equity
Offered From the posting source checked Jun 20, 2026

Application

Cover letter
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Assessment
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Deadline
Not stated

Where they hire

State eligibility is not yet verified.

About this role

PhD GenAI Research Scientist Intern San Francisco, California Company Description: At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems, from security threat detection to cancer drug development. We do this by building and running the world's best data and AI platform, so our customers can focus on the high value challenges that are central to their own missions. The Mosaic AI organization enables companies to develop AI models and systems using their own data, with technologies ranging from fine-tuning LLMs for enterprise domains, to a platform for building compound AI systems that use retrieval and agents. Mosaic AI is committed to the belief that a company's AI models are just as valuable as any other core IP, and that high-quality AI models should be available to all. Job description: Most of the world's data+AI problems lie in enterprise domains, behind closed doors. Our research team's goal is to push the frontier of "domain adaptation" - how can we develop LLMs and AI systems that work well for custom domains. To do this we are tackling open research problems on a range of topics, from how to scale/automate eval, fine tune with synthetic data, retrieval augmentation, fast/efficient inference and more. You will work with our research team on projects focused on adapting LLMs and AI systems towards enterprise domains. This may include: - Adapting, improving, and evaluating a method from the literature. - Designing an entirely new method for domain adaptation. - Composing together multiple

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