📍
San Francisco, CA

GenAI Research Scientist

No experience
Technology & Digital
Software engineering
Posted:
December 29, 2025

Databricks

Data intelligence platform for businesses
77.5
Palpable Score
Apply >view company >

Job Description

As a Research Scientist on the GenAI Team at Databricks, you will be responsible for keeping up with the latest developments in deep learning and advancing the scientific frontier by creating new techniques that go beyond the state of the art. You will work together on a collaborative team of researchers with diverse backgrounds and technical training. And most importantly, you will love our customers: our goal is to make our customers successful when they train large models, and we encode our scientific expertise into our products to make that possible.

You might be a good fit if you…

Your Responsibilities

About the company

Databricks

Company overview
Databricks builds a cloud platform used by data teams to run analytics, data engineering, and AI workloads in one place, often described as a “lakehouse” approach. The company sells to enterprises that want to unify data pipelines, BI, and machine learning on major clouds. Databricks is closely associated with Apache Spark’s origins and supports open-source projects used widely in data engineering and ML workflows. The company’s customers range from large enterprises to fast-growing tech companies building data products.

Locations and presence

Databricks is headquartered in San Francisco and lists many offices across the Americas, EMEA, and Asia-Pacific. The company describes a hybrid setup built around weekly “Team Day” in-office time, with most work happening from home, and notes that some roles are fully remote.

Palpable Score

77.5
/ 100
Databricks offers clear, recurring entry points for students and new grads, and the company backs that up with cohort-style roles and explicit mentorship language in early-career job postings. Databricks also publishes candidate-facing guidance on the hiring process and shares pay bands on at least some early-career roles, which is a practical signal of transparency. The main limiter is mixed public candidate and employee feedback on consistency, especially around responsiveness and clarity of progression, and the lack of published early-career outcomes data.
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