Strategic Overview
Staff Software Engineer, AI Search at Databricks is a dev opportunity centered on the specialist responsibilities and outcomes described in the employer's listing.
The published role emphasizes work such as Drive the architecture and evolution of the Search product, delivering scalable, low-latency, and high-quality retrieval across massive multimodal datasets. Partner with infrastructure, product, and research teams to shape APIs and developer experiences that…
The role is listed for Mountain View, California with the working arrangement published with the role, giving candidates the practical context needed to assess fit before applying.
Key information at a glance
About This Role
RDQ427R351 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.
Search plays a foundational role in this mission. Whether through keyword-based retrieval, semantic similarity via vector embeddings, or hybrid approaches that combine both, our Search technologies help customers find, discover, and understand information across massive, complex datasets. These capabilities power everything from Retrieval Augmented Generation (RAG), AI assistants, and recommendation systems to…
As a Staff Engineer for Search, you’ll play a critical role in building and scaling our next-generation Search product — spanning Vector Search, Keyword Search, and Search Quality. You’ll drive the design and evolution of a highly-performant, cost-efficient, and developer-friendly Search stack that enables customers to build AI applications with accuracy, speed, and scale. Beyond hands-on contributions, you’ll help…
Key Responsibilities & Objectives
- Drive the architecture and evolution of the Search product, delivering scalable, low-latency, and high-quality retrieval across massive multimodal datasets.
- Partner with infrastructure, product, and research teams to shape APIs and developer experiences that make it easy to build production-grade, AI-powered and search-driven applications.
- Lead end-to-end engineering efforts — from design to production rollout — ensuring high standards for performance, correctness, and reliability.
- Make direct, high-impact contributions to core systems powering search, discovery, and recommendation experiences.
Required Qualifications & Candidate Blueprint
- 10+ years of experience building and operating large-scale distributed systems.
- Deep expertise in Search technologies (vector, keyword, hybrid) and related domains such as indexing, ranking, retrieval infrastructure, and relevance optimization.
- Familiarity with storage systems, recommendation engines, and database internals.
Salary & Benefits
Published compensation: $166,000 – $225,000 USD
- Strategic and product-oriented mindset with the ability to align technical execution with long-term vision.
Salary Benchmark
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Benchmark uses active employer job postings in our database and excludes this listing. It is not a third-party salary estimate, and currencies are not converted.
Full Job Description
The following is the complete job description supplied by the source. We sanitize unsafe markup and remove external links, but do not shorten or rewrite the employer's source content.
RDQ427R351
At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.
Search plays a foundational role in this mission. Whether through keyword-based retrieval, semantic similarity via vector embeddings, or hybrid approaches that combine both, our Search technologies help customers find, discover, and understand information across massive, complex datasets. These capabilities power everything from Retrieval Augmented Generation (RAG), AI assistants, and recommendation systems to enterprise knowledge management, in-product search, and data exploration.
As a Staff Engineer for Search, you’ll play a critical role in building and scaling our next-generation Search product — spanning Vector Search, Keyword Search, and Search Quality. You’ll drive the design and evolution of a highly-performant, cost-efficient, and developer-friendly Search stack that enables customers to build AI applications with accuracy, speed, and scale. Beyond hands-on contributions, you’ll help define the long-term vision, mentor senior engineers, collaborate across teams, and lead strategic efforts that expand the technical and business impact of Search at Databricks.
Check out our recent blog post: Decoupled by Design: Billion-Scale AI Search
The impact you will have:
- Drive the architecture and evolution of the Search product, delivering scalable, low-latency, and high-quality retrieval across massive multimodal datasets.
- Identify and solve challenges in indexing, storage, query execution, and ranking — blending innovations in vector search, keyword search, and hybrid retrieval techniques.
- Partner with infrastructure, product, and research teams to shape APIs and developer experiences that make it easy to build production-grade, AI-powered and search-driven applications.
- Lead end-to-end engineering efforts — from design to production rollout — ensuring high standards for performance, correctness, and reliability.
- Make direct, high-impact contributions to core systems powering search, discovery, and recommendation experiences.
- Champion software engineering excellence and mentor other engineers through design reviews, technical discussions, and collaborative development.
- Contribute to long-term planning and influence Databricks’ technical direction in search, AI infrastructure, and data systems.
What we look for:
- 10+ years of experience building and operating large-scale distributed systems.
- Deep expertise in Search technologies (vector, keyword, hybrid) and related domains such as indexing, ranking, retrieval infrastructure, and relevance optimization.
- Familiarity with storage systems, recommendation engines, and database internals.
- Strong foundation in algorithms, data structures, and system design as applied to large-scale retrieval and data systems.
- Proven ability to deliver technically complex, high-impact initiatives that create customer or business value.
- Experience leading architecture for performance-sensitive, large-scale systems.
- 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.
Pay Range Transparency
Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.
About Databricks
Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Editor’s Market Perspective (Our Custom Analysis)
Why This Position Matters
This dev opening is a useful opportunity for candidates whose experience matches the role-specific capabilities named in the listing. Its strongest career signal is the scope of the work itself — particularly Drive the architecture and evolution of the Search product, delivering scalable, low-latency, and high-quality retrieval across massive multimodal datasets. — rather than a generic title alone.
Resume & Application Strategy
For the application, mirror the employer's language where it truthfully matches your background: foreground the role-specific capabilities named in the listing, quantify outcomes from comparable work, and place the most relevant experience near the top of the resume. If the listing specifies tools, platforms, leadership scope, or domain experience, use those exact terms in context instead of presenting an unrelated keyword list.
We organize source-provided information into this independent job-seeker overview. Compensation is shown only when the source provides it; the official listing remains the authority for current requirements and application instructions.