Staff Software Engineer, Search Quality

RDQ326R95

At Databricks we are passionate about enabling data teams to solve the worlds 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 worlds 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. Youll 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. Youll 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.

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 longterm 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.

Required Experience:

Staff IC