About the Role
The AI Platform team at Databricks is dedicated to building the infrastructure that powers machine learning and AI at scale. Their products cover the entire ML lifecycle, from feature engineering and model training to model serving and monitoring, enabling data and AI teams to confidently build, deploy, and operate production ML systems. This team works on technically demanding areas such as recommendation systems, real-time inference, large-scale distributed training, LLM infrastructure, vector search, and feature stores.
The mission is to simplify the process for enterprises to deploy AI into production by offering a unified, governed, and performant AI platform. This platform deeply integrates with the Databricks Data Intelligence Platform, connecting MLflow, Unity Catalog, Model Serving, Vector Search, Feature Engineering, LLM, and Agent infrastructure into a cohesive experience. As part of this team, you will drive the vision and roadmap for AI platform product areas, defining how customers build, train, deploy, and monitor AI and ML systems on Databricks. You will collaborate with engineering teams to deliver an integrated and powerful path from experimentation to production.
The Impact You Will Have
- Own the product roadmap for AI platform areas, defining what, why, and in what order products are built to accelerate customer adoption of AI and ML in production.
- Drive strategy for key AI platform capabilities, shaping how enterprises operationalize AI at scale.
- Partner closely with engineering teams to make deeply technical decisions regarding ML infrastructure, from distributed training architectures to real-time serving systems.
- 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.
- Collaborate with GTM, Solutions Architecture, and Customer Success teams to drive enterprise adoption, shape field enablement, and inform competitive positioning.
- Define pricing, packaging, and commercialization strategy for AI platform features, working with business teams to maximize value capture.
- 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. Comfortability with system architecture, writing technical specs, and engaging credibly with world-class ML engineers is essential.
- 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 — a history of shipping platform products, driving commercial outcomes, and working with field teams to land enterprise deals.