Qualifications
Key Responsibilities:Lead the design and implementation of ML-based search and discovery relevance models and systems that integrate with Databricks' offerings. Create and automate ML and NLP pipelines for data preprocessing, query understanding, ranking, retrieval, and model evaluation to foster rapid experimentation. Work closely with product managers and cross-functional teams to spearhead technology-driven initiatives that enable innovative business strategies and product roadmaps for enhanced search experiences. Help establish a robust framework for evaluating improvements in search ranking, both offline and online. What We’re Looking For:A Bachelor’s degree (Master’s or PhD preferred) in Computer Science or a related discipline. Over 5 years of experience in developing scalable search relevance systems in production or high-impact research settings. Familiarity with applying LLM to search relevance. Experience in areas such as:Query understandingNLPText miningRecommendationsPersonalizationDiscoveryConversational AIStrong grounding in computer science fundamentals. Contributions to popular open-source projects.
About the job
The Applied AI team at Databricks is at the cutting edge of AI and ML-driven products. Our customers constantly create a variety of assets (such as tables, notebooks, dashboards, and ML models) on our platform, making the search for these assets a vital aspect of their experience.
As our Search product continues to grow, we are looking for multiple Machine Learning Engineers at various experience levels to enhance our Search Quality. In 2026, our primary focus will be on improving search ranking, query comprehension, developing robust evaluations, and expanding asset coverage to facilitate seamless searching at scale.
About Databricks
At Databricks, we are pioneering state-of-the-art AI solutions that transform how users interact with data and our products. Join us to shape the future of AI-driven technology and create unparalleled user experiences.