Staff Applied AI Engineer

Drata is a leading company in compliance software innovation, dedicated to building trust and security across the internet. They are looking for a Staff Applied AI Engineer to enhance the quality and effectiveness of their AI systems through research, experimentation, and evaluation, focusing on compliance automation.

Responsibilities:

• 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
• 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
• Run experiments to validate hypotheses and quantify improvements before production rollout
• 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
• Stay current on applied research in RAG, agents, LLM evaluation, and relevance modeling; bring innovations into the product

Requirements:

• 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
• Experience with RAG systems: chunking strategies, vector databases, retrieval optimization
• Proficiency in evaluation methodology: metrics design, golden dataset creation, A/B testing, statistical analysis
• Strong Python skills and comfort with notebook-driven research workflows
• Experience communicating research findings to engineering teams and translating insights into actionable recommendations
• Experience with compliance, legal, or document-heavy domains
• Publications or contributions in IR, NLP, or RAG evaluation