# Staff Software Engineer, Search Quality

**Company:** [Databricks](https://hotfix.jobs/companies/databricks)
**Location:** Mountain View, CA
**Role:** Fullstack Engineering
**Salary:** $165k – $220k/yr
**Experience:** 10+ years
**Skills:** Ranking Models, Vector Search, Keyword Search, Hybrid Retrieval, Query Understanding, Relevance Metrics, Evaluation Frameworks, Neural Ranking, Machine Learning, System Design
**Posted:** 2026-01-30

> 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.

## Job Description

## 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.

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