Description
At Databricks, we are enabling data teams to solve the world's toughest problems by building and running the world's best data and AI infrastructure platform.来たSearch plays a foundational role in this mission, powering 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.
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.