Staff Software Engineer, Search Quality
Databricks · Mountain View, California · Engineering - Pipeline · listed November 18, 2025
The shape of it
Seniority
Staff
Experience asked
10+ years
Where
Hybrid
Stated pay
$165,300 – $219,675 USD
Requirements listed
9
Length
876 words
In the posting’s own words
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.
What it asks for · 9
- 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.
What the job covers
- 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.
Tools and skills named
Data
- Databricks5×
- Experimentation
Models & research
- Machine learning2×
Product & design
- Prototyping
- Roadmap
Ways of working
- Mentorship2×
Words the posting leans on
- search14×
- retrieval11×
- ranking10×
- quality9×
- evaluation7×
- relevance7×
- product6×
- systems6×
- data5×
- technical5×
- design4×
- models4×
- engineers3×
- search quality3×
- vector3×
- architecture2×
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