Staff Software Engineer, Search Quality

P-150

At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.

Search plays a foundational role in this mission. Whether through keyword-based retrieval, semantic similarity via vector embeddings, or hybrid approaches that combine both, our Search technologies help customers find, discover, and understand information across massive, complex datasets. These capabilities power everything from Retrieval Augmented Generation (RAG), AI assistants, and recommendation systems to 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 our Search stack delivers accurate, high-quality results across diverse multimodal datasets and query patterns. You’ll work across research, product, and infra to push the frontier of retrieval quality for enterprise AI applications — blending traditional information retrieval techniques, representation learning, and neural ranking.

Beyond hands-on contributions, you will help define our long-term vision for relevance and quality, mentor senior engineers, and lead strategic efforts that raise the accuracy, reliability, and product impact of Search across Databricks.

Check out our recent blog post: Reranking in Databricks AI Search for Faster, Smarter Retrieval in RAG Agents

The impact you will have:

• 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.
• Build and operate resilient, low-latency services for ranking, evaluation, and relevance signal processing.
• Champion excellence in ML and search engineering, mentoring teammates and elevating design, code quality, and scientific rigor across the team.
• Shape Databricks’ long‑term roadmap for retrieval quality, ranking infrastructure, and the foundations for retrieval-driven AI products.

What we look for:

• 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 with clear business or product outcomes.
• Strong communication skills and ability to collaborate across teams in fast-moving environments.
• Strategic and product-oriented mindset with the ability to align technical execution with long-term vision.
• Passion for mentoring, growing engineers, and fostering technical excellence.