Staff Software Engineer, Search Quality

Leads technical vision and development of search quality systems, including ranking models, relevance evaluation, and hybrid retrieval for enterprise AI applications. Requires 10+ years experience in large-scale search and ML-driven relevance systems.

About the job

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.

Skills

Ranking Models, Vector Search, Keyword Search, Hybrid Retrieval, Query Understanding, Relevance Metrics, Evaluation Frameworks, Neural Ranking, Machine Learning, System Design