Staff Applied AI Engineer

Drata is the proof layer that helps companies earn and keep the trust of their users, customers, partners, and prospects. As a Staff Applied AI Engineer you'll work at the intersection of LLMs, retrieval systems, agentic workflows, and real-world compliance challenges, partnering closely with product, platform, and security teams to design and evaluate the information access and reasoning strategies that power compliance automation.

What you'll do:

• Design and evaluate information access + reasoning strategies across RAG, agents, and classic ML: chunking, embedding models, hybrid search, metadata filtering, structured retrieval, tool use, and multi-step workflows
• Design and own production AI systems end-to-end (LLM pipelines, RAG, reranking, vector stores, orchestration)
• Prototype GenAI workflows (including agentic systems) that map and reason over compliance objects (controls <-> risks <-> requirements)
• Explore ML + probabilistic approaches where GenAI is not the best fit: classifiers, ranking models, graph/link prediction, calibration, and weak supervision
• Build and maintain evaluation frameworks: golden datasets, automated quality metrics, regression detection
• Implement and tune ranking/reranking systems: cross-encoders, LLM-based rerankers, learning-to-rank, custom scoring functions
• Debug failure modes and build error taxonomies across retrieval, reasoning, and generation
• Collaborate with AI and Software Engineers to hand off validated approaches for productionization

What you'll bring:

• 10+ years of experience in applied research, data science, or ML with a focus on NLP, information retrieval, or knowledge systems
• 2+ years of hands-on experience building or contributing to production AI/ML systems
• Strong foundation in information retrieval: dense and sparse retrieval, embedding models, search relevance
• Deep experience with RAG, embeddings, reranking, vector databases (Pinecone, FAISS, Chroma, etc.), and agentic workflows
• Experience designing evaluation frameworks and using quantitative analysis to improve system performance
• Strong Python skills (TypeScript is a plus)

Location: Remote across the U.S.; hybrid option from the San Francisco office.