Staff Software Engineer, Search Quality

Databricks
Mountain View, San Francisco
Workplace: OnsiteFull timeFunction: Software EngineeringSkills: ["Communication","Mentoring","Leadership","Collaboration"]

Senior-level software engineer leading the direction of search quality for Databricks’ next-gen search product. You will design ranking and relevance architectures, build production ranking models, and collaborate with research, product, and infra to raise accuracy, reliability, and impact of search across multimodal datasets, while mentoring engineers and shaping long-term retrieval strategy.

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FursaFursa
Databricks
Databricks
9 months ago

Staff Software Engineer, Search Quality

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Source: Company careers pageValidated by: Fursa AI
Last checked: 3 hours agoStatus: Live

Job Summary

Senior-level software engineer leading the direction of search quality for Databricks’ next-gen search product. You will design ranking and relevance architectures, build production ranking models, and collaborate with research, product, and infra to raise accuracy, reliability, and impact of search across multimodal datasets, while mentoring engineers and shaping long-term retrieval strategy.
Location: Mountain View, San Francisco
Workplace: Onsite
Employment Type: Full time
Job Function: Software Engineering
Seniority: Sr. Manager level

Key Responsibilities

  • •Lead the technical vision for Search Quality, shaping the ranking architecture, relevance modeling stack, and evaluation systems that power Databricks’ next-generation retrieval experiences.
  • •Identify and solve challenges in ranking, query understanding, and hybrid retrieval — advancing state-of-the-art techniques in vector, keyword, and multimodal search.
  • •Design and train production-ready ranking and reranking models with strong guarantees around quality, latency, and resource efficiency.
  • •Partner closely with research, product, and infra teams to define metrics, evaluation methodologies, and experimentation strategies for new retrieval features and model architectures.
  • •Drive end-to-end engineering efforts — from early prototyping to production rollout — ensuring correctness, reliability, and measurable improvements to relevance.

Key Requirements

  • •10+ years of experience building large-scale search, ranking, recommendation, or ML-driven relevance systems.
  • •Deep expertise in Search Quality, including ranking models, signals, query understanding, and evaluation methodologies.
  • •Strong understanding of relevance metrics and evaluation frameworks.
  • •Familiarity with vector search, keyword search, hybrid retrieval, and embedding-based semantic retrieval.
  • •Solid foundation in algorithms, data structures, and system design for performance-critical ranking and retrieval systems.
Experience:SearchMLRelevanceRankingRetrieval
Skills:CommunicationMentoringLeadershipCollaboration
Languages:English
Tech Stack:Vector searchEmbeddingNeural rankingRanking modelsInformation retrievalLatency optimizationProductionMachine learning

Company Brief

Databricks
Provides a unified data analytics platform powered by Apache Spark to simplify building, deploying, and scaling data engineering, data science, and machine learning workloads for enterprises.
Industry: Data Infrastructure
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Scaleup
Valuation: Unicorn (USD 1B+)
Funding: Series E+
Headquarters: San Francisco, United States
Founded: 2013
WebsiteLinkedIn