Applied AI Scientist

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.
• 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.
• Raise technical rigor: share patterns, mentor, and accelerate decisions across teams.

Requirements

• Must have 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.
• Strong experimentation and evaluation rigor: offline evals, A/B testing, causal thinking.

Qualifications

• IC5 signals: set technical direction across teams, owned architecture, and operated as a peer-level influence, not just a strong executor.
• 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.

Skills

• Strong coding ability and real comfort in a production codebase.
• Strong experimentation and evaluation rigor: offline evals, A/B testing, causal thinking.
• 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.

Benefits

• Our commitment to diversity, equity, and inclusion at Demandbase.
• Unsolicited Submissions: We are not accepting unsolicited resumes or outreach from third-party recruiting agencies.

Pay

Competitive salary commensurate with experience.

Schedule

Full-time.