Staff Product Manager, AI Platform

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 a cohesive experience. You will join a team that ships products used by thousands of the world's most sophisticated data and AI organizations.

You will drive the vision and roadmap for AI platform product areas and define how customers build, train, deploy, and monitor AI and ML systems on Databricks. You will collaborate across engineering teams to deliver an integrated and powerful path from experimentation to production.

The impact you will have:

• 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.


• You will grow end-user engagement with Databricks AI tools by identifying adoption bottlenecks and partnering cross-functionally to remove them.

What we look for:

• 5+ 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.


• Proven enterprise B2B product management experience with highly technical customers — you have shipped platform products, driven commercial outcomes, and worked with field teams to land enterprise deals.