Staff Software Engineer Search Quality
Databricks is a data and AI platform that lets organizations build analytics, AI agents, and applications on a unified, governed lakehouse.
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 set the technical direction for Search Quality and build ranking, relevance, evaluation, and low-latency retrieval systems. You will develop production ranking and reranking models, define evaluation methods and experimentation strategies, lead initiatives from prototype through rollout, and mentor engineers.
Requirements
- 10+ years of experience building large-scale search, ranking, recommendation, or ML-driven relevance systems
- Expertise in search quality, ranking models, signals, query understanding, and evaluation methodologies
- Understanding of relevance metrics and evaluation frameworks
- Familiarity with vector search, keyword search, hybrid retrieval, and embedding-based semantic retrieval
- Knowledge of algorithms, data structures, and system design for performance-critical ranking and retrieval systems
- Ability to deliver high-impact technical initiatives
- Communication and cross-functional collaboration skills
- Ability to align technical execution with long-term vision
- Mentoring experience
Responsibilities
- Lead the technical vision for Search Quality, including ranking architecture, relevance modeling, and evaluation systems
- Solve ranking, query-understanding, and hybrid-retrieval challenges
- Design and train production-ready ranking and reranking models
- Define metrics, evaluation methodologies, and experimentation strategies
- Drive engineering efforts from prototyping through production rollout
- Build and operate resilient, low-latency services for ranking, evaluation, and relevance signal processing
- Mentor teammates and elevate engineering and scientific rigor
- Shape the long-term roadmap for retrieval quality and ranking infrastructure
Benefits
- Annual performance bonus eligibility
- Equity eligibility