Demandbase
Applied AI Scientist
San Francisco, California · Staff+ · Full-time
Sponsorship not specifiedDetected 104 days ago
Machine LearningData ScienceNLPLLMsRAGA/B TestingResearch
About the role
- We're hiring a senior IC who pairs deep research training with the ability to ship.
- You'll work on hard reasoning, retrieval, and orchestration problems, and you'll take them all the way into the product.
- This is research-informed product work: novel thinking is expected, but prototypes are the start, not the deliverable.
Responsibilities
- Design grounding and context assembly: retrieval pipelines, context selection, query understanding.
- Build structured representations that help the system reason over accounts, people, and activity.
- You take a nebulous problem, give it structure, build something testable, and iterate.
Requirements
- 7+ years in applied ML, NLP, IR, or ranking (research-track and graduate work can count toward depth), with recent hands-on ownership of LLM and agentic systems.
Nice to have
- Query understanding and intent modeling.
Company info
- Demandbase is the only pipeline AI platform that empowers GTM teams to automate growth at scale.
- With a unified view of data, insights, actions, and outcomes, B2B enterprises can seamlessly align and execute their account-based GTM strategies with confidence.
- Thousands of businesses trust Demandbase to maximize revenue, minimize waste, and consolidate their data and tech stacks - all in one platform.
- As a company, we're as committed to growing careers as we are to building world-class technology.
- We invest heavily in people, our culture, and the community around us.
- We have also continuously been recognized as One of The Best Places To Work in the San Francisco Bay Area by Fortune, and One of The 60 Best Companies To Sell For by Selling Power.
- Our offices are located in San Francisco, New York, Austin, Seattle, India, and the United Kingdom.
- Applied AI Scientist (Staff level)
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This listing is sourced directly from Demandbase's careers page and normalized into a canonical job model.