Staff Software Engineer, Search Quality

At Databricks, we are passionate about enabling data teams to solve the world's toughest problems by building and running the world's best data and AI infrastructure platform. Search plays a foundational role in this mission — through keyword-based retrieval, semantic similarity via vector embeddings, and hybrid approaches — powering Retrieval Augmented Generation (RAG), AI assistants, recommendation systems, enterprise knowledge management, in-product search, and data exploration.

As a Staff Software Engineer for Search Quality, you will drive the technical direction of ranking, relevance, evaluation, and quality initiatives across Databricks' next-generation Search product. You'll design and build the systems, models, and evaluation frameworks that ensure accurate, high-quality results across diverse multimodal datasets and query patterns, working across research, product, and infra to push the frontier of retrieval quality for enterprise AI applications.

• Lead the technical vision for Search Quality, shaping ranking architecture, relevance modeling stack, and evaluation systems.
• Identify and solve challenges in ranking, query understanding, and hybrid retrieval.
• Design and train production-ready ranking and reranking models with guarantees around quality, latency, and resource efficiency.
• Partner with research, product, and infra teams to define metrics, evaluation methodologies, and experimentation strategies.
• Drive end-to-end engineering efforts from prototyping to production rollout.
• Build and operate resilient, low-latency services for ranking, evaluation, and relevance signal processing.
• Mentor teammates and elevate design, code quality, and scientific rigor.
• Shape Databricks' long-term roadmap for retrieval quality and ranking infrastructure.

• 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.
• Proven ability to deliver high-impact technical initiatives.
• Strong communication and collaboration skills.

Local pay range $165,300—$219,675 USD. Total compensation may include annual performance bonus, equity, and benefits.