# Staff Software Engineer, Search Quality

**Company**: Databricks
**Location**: Mountain View, California
**Work arrangement**: onsite
**Experience**: staff
**Job type**: full-time
**Salary**: $165,300-$219,675 USD
**Category**: Engineering
**Industry**: Technology
**Wikidata**: https://www.wikidata.org/wiki/Q18350420

**Apply**: https://job-boards.greenhouse.io/databricks/jobs/8295792002
**Canonical**: https://yubhub.co/jobs/job_deb98db6-eba

## 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.

## Skills

### Required
- large-scale search
- ranking
- recommendation
- ML-driven relevance systems
- Search Quality
- ranking models
- signals
- query understanding
- evaluation methodologies
- relevance metrics
- evaluation frameworks
- vector search
- keyword search
- hybrid retrieval
- embedding-based semantic retrieval
- algorithms
- data structures
- system design