Staff Software Engineer - Machine Learning (Search)

<p>P-1408</p> <p><strong>The Applied AI team at Databricks sits at the forefront of advancing AI/ML-powered products</strong>. Databricks’ customers are continuously creating new assets (tables, notebooks, dashboards, datarooms, pipelines, sql queries, ml models etc.) on the platform. Some of them can have hundreds of millions of assets. Finding an asset is a critical user journey for Databricks’ customers which helps them accomplish their tasks.&nbsp;</p> <p>As our Search product continues to evolve, we are seeking a <strong>Staff Engineer </strong>to lead enhancements to our Search Quality. In 2025, we will focus on <strong>enhancing search ranking, improving query understanding, building robust evals</strong> and growing the coverage of assets to enable seamless search at scale.</p> <p>&nbsp;</p> <p><strong>Key Responsibilities</strong></p> <ul> <li>Drive the <strong>development and deployment of ML based search and discovery relevance models</strong> and systems integrated with Databricks' products and services.&nbsp;</li> <li>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.&nbsp;</li> <li>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.&nbsp;</li> <li>Contribute to building a robust framework for evaluating search ranking improvements - both offline and online.</li> </ul> <p>&nbsp;</p> <p><strong>What We’re Looking For</strong></p> <ul> <li>BS+ (M.S. or PhD preferred) in Computer Science, or a related field.</li> <li>10+ years experience developing search relevance systems at scale in production or in high-impact research environments.</li> <li>Experience applying LLM to search relevance</li> <li>Experience in one or more of the following:&nbsp;&nbsp;</li> <ul> <li>