> Markdown version of [/jobs/ext/3967191-applied-ai-scientist](https://www.wearedevelopers.com/jobs/ext/3967191-applied-ai-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Applied AI Scientist - **Company:** Demandbase - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Graph Database, Natural Language Processing, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Feature Extraction - **Published:** October 27, 2026 - **Apply:** https://www.careerbuilder.com/job-details/applied-ai-scientist-san-francisco-ca--aed9a726-614e-4a5a-980d-876cffabbbeb ## About the Role Artificial Intelligence (AI), Artificial Intelligence (AI), Business-to-Business (B2B), Sales, Prototyping, Product Engineering, Mentoring, Natural Language Processing (NLP), IR (Infrared), Product Demonstration, * PhD in CS, ML, NLP, IR, or a related field. * 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. * Shipped LLM or RAG-style systems into production and personally owned their reliability, latency, and monitoring at scale. * Built and owned agentic or multi-step orchestration (tool use, routing, planning), not just prototypes or demos. * Strong coding ability and real comfort in a production codebase. * Solid experimentation and evaluation rigor: offline evals, A/B testing, causal thinking. * IC5 signals: set technical direction across teams, owned architecture, and operated as a peer-level influence, not just a strong executor. Strong plus (any of these stand out, none are required) * Knowledge graphs, entity resolution, or representation learning. * Query understanding and intent modeling. * Search and retrieval depth: lexical, semantic, hybrid, reranking. * LLM fine-tuning. * A record of taking research from idea to published result to shipped feature. ## Description 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. You'll be a peer to our senior technical leaders and a close partner to product and engineering. What you'll do * Design grounding and context assembly: retrieval pipelines, context selection, query understanding. * Improve orchestration and reasoning across multi-step workflows: tool use, routing, decomposition, evaluation. * Build structured representations that help the system reason over accounts, people, and activity. * Bring research-grade rigor to ambiguous problems, then translate it with engineering into reliable, production-ready capabilities. You won't be a full-time SWE, but you write real code that holds up in production. * Raise technical rigor: share patterns, mentor, and accelerate decisions across teams.