Das ist der Job
Some of them can have hundreds of millions of assets.
Darum lohnt es sich
P-1408 The Applied AI team at Databricks sits at the forefront of advancing AI/ML-powered products. Collaborate with product managers and cross‑functional teams to drive technology-first initiatives that enable novel business strategies and product roadmaps for the search and discovery experience.
Benefits Opportunity to shape the future of AI-driven products at Databricks. Work with cutting‑edge models and collaborate with a world‑class team of AI and ML experts. #J-18808-Ljbffr Databricks’ customers are continuously creating new assets—tables, notebooks, dashboards, datarooms, pipelines, sql queries, ml models, etc.—on the platform.
Finding an asset is a critical user journey for Databricks’ customers which helps them accomplish their tasks. In 2025, we will focus on enhancing search ranking, improving query understanding, building robust evals and growing the coverage of assets to enable seamless search at scale.
Key Responsibilities Drive the development and deployment of ML-based search and discovery relevance models and systems integrated with Databricks' products and services.
Design and implement automated ML and NLP pipelines for data preprocessing, query understanding and rewrite, ranking and retrieval, and model evaluation, enabling rapid experimentation and iteration. Contribute to building a robust framework for evaluating search ranking improvements—both offline and online.
What We’re Looking For BS+ (M.S. or Ph.D. preferred) in Computer Science, or a related field. 10+ years experience developing search relevance systems at scale in production or in high-impact research environments. Experience applying LLM to search relevance.
Experience in one or more of the following: query understanding, NLP, text mining, recommendations, personalization, discovery, conversational AI. Strong understanding of computer science fundamentals. Contributions to well-used open-source projects.
