Associate Product Manager: AI Platform

Databricks

San Francisco (CA)

On-site

USD 133,000 - 150,000

Full time

14 days+
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Job summary

Databricks is seeking an Associate Product Manager for our 2027 APM cohort in San Francisco, Mountain View, and Bellevue. You will join a team collaborating with product managers, engineers, designers, account teams, and leaders to understand customer problems and drive solution development across AI Platform, Genie, ML, Unity Catalog, Databricks SQL, ETL, Streaming, and EDA.

You will own projects end-to-end—design, build, launch, and iterate based on feedback—while shipping high-quality

Qualifications

  • Graduating Fall 2026 or Spring 2027 with CS or related engineering degree.
  • Pursuing a CS or related engineering field.
  • Experience using AI tooling for personal projects.
  • Some experience with SQL and/or Python.
  • Hands-on and ready to build, test and iterate.
  • Analytical skills to make data-driven decisions.
  • Ability to explain complex topics clearly to partners.
  • Curious, collaborative and eager to learn.

Responsibilities

  • Understand customer problems and establish viable solution rails.
  • Prototype and test early ideas with customers and engineers.
  • Work with engineers and designers to ship features on the Databricks platform.
  • Own projects end-to-end—from design to launch and iteration.
  • Build with scale, quality, and security in mind from day one.

Skills

AI tooling usage
SQL
Python
Analytical thinking
Hands-on learning
Communication

Education

Bachelor's/Master's in CS or related

Job description

Databricks is seeking an Associate Product Manager for our 2027 APM cohort in San Francisco, Mountain View, and Bellevue. You will join a team collaborating with product managers, engineers, designers, account teams, and leaders to understand customer problems and drive solution development across AI Platform, Genie, ML, Unity Catalog, Databricks SQL, ETL, Streaming, and EDA.

You will own projects end-to-end—design, build, launch, and iterate based on feedback—while shipping high-quality

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