Staff Product Manager, AI Platform
Databricks - San Francisco, California
Posted Feb 18, 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
- 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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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Deadline
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Where they hire
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About this role
Staff Product Manager, AI Platform San Francisco, California RDQ427R276 Location: San Francisco or Seattle 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 infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers - and customer obsessed - we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. More about the team: The AI Platform team builds the infrastructure that powers machine learning and AI at scale on Databricks. Our products span the full ML lifecycle - from feature engineering and model training to model serving and monitoring - enabling data and AI teams to build, deploy, and operate production ML systems with confidence. We work across some of the most technically demanding areas in the platform, including recommendation systems, real-time inference, large-scale distributed training, LLM infrastructure, vector search, and feature stores. Our mission is to make it radically easier for enterprises to put AI into production. We do this by providing a unified, governed, and performant AI platform that integrates deeply with the Databricks Data Intelligence Platform - connecting MLflow, Unity Catalog, Model Serving, Vector Search, Feature Engineering, LLM, and Agent infrastructure into
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