Staff Product Manager, AI PlatformActive$182K

Databricks
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The opportunity

At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI…

What you'll do

  • You will own the product roadmap for AI platform areas: defining what we build, why, and in what order — to accelerate customer adoption of AI and ML in production.

  • You will drive strategy for key AI platform capabilities, shaping how enterprises operationalize AI at scale.

  • You will partner closely with engineering teams to make deeply technical: decisions about ML infrastructure — from distributed training architectures to real-time serving systems.

  • You will represent the voice of the customer by engaging directly with: enterprise ML teams, translating their pain points and workflows into platform capabilities that simplify the path to production AI.

  • You will collaborate with GTM, Solutions Architecture, and Customer Success: teams to drive enterprise adoption, shape field enablement, and inform competitive positioning.

  • You will define pricing, packaging, and commercialization strategy for AI: platform features, working with business teams to maximize value capture.

What they're looking for

  • You will grow end-user engagement with Databricks AI tools by identifying: adoption bottlenecks and partnering cross-functionally to remove them.
  • + years of experience as a Product Manager working on platform or: infrastructure products, ideally in ML/AI, data, or cloud services.
  • Deep technical background: CS, EE, or equivalent degree strongly preferred; former software engineer experience is a significant plus. You should be comfortable going deep on system architecture, writing technical specs, and engaging credibly with world-class ML engineers.
  • Experience with ML/AI infrastructure, data platforms, or cloud services: (e.g., model training, model serving, feature stores, vector search, LLM infrastructure, ML pipelines, or similar systems). Familiarity with recommendation systems is a bonus.