Staff Software Engineer Search Platform
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
Maintainer signals as of 9/23/2026
Funding history
Investors
About Databricks
Data engineers, analysts, and AI teams use Databricks to process large datasets, build reliable pipelines, and train models on a single governed platform. Users can run SQL analytics, serve ML predictions in real time, and deploy AI agents grounded in enterprise data. Its open lakehouse architecture provides consistent security and governance across analytical and operational workloads.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will develop and deploy machine-learning search and discovery relevance models. You will build automated ML and NLP pipelines for data preprocessing, query understanding, rewriting, ranking, retrieval, and model evaluation. You will improve search-ranking evaluation through offline and online frameworks and work with cross-functional partners on search initiatives.
Requirements
- BS or higher degree in Computer Science or a related field.
- 10+ years of experience developing search relevance systems at scale in production or high-impact research environments.
- Experience applying LLMs to search relevance.
- Experience with query understanding, NLP, text mining, recommendations, personalization, discovery, or conversational AI.
- Strong understanding of computer science fundamentals.
- Contributions to well-used open-source projects.
Responsibilities
- Drive the development and deployment of ML-based search and discovery relevance models and systems.
- Design and implement automated ML and NLP pipelines for preprocessing, query understanding, rewriting, ranking, retrieval, and model evaluation.
- Collaborate with product managers and cross-functional partners on search and discovery initiatives.
- Build frameworks to evaluate offline and online search-ranking improvements.