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Salary: USD 157,000 - 252,000 / annual
Drata is seeking a Senior Applied AI Engineer to drive the quality and effectiveness of AI systems through rigorous experimentation, evaluation, and applied research. This research-focused role emphasizes experimentation and rigor over production engineering. You will own the science behind how Drata's AI products retrieve, reason, and respond—working closely with AI and Software Engineers to turn validated approaches into production-ready systems.
Drata's compliance platform is document-heavy, with systems including VRM Agent, AIQA, Trust Agent, and policy-to-control mappers that all depend on high-quality information retrieval and reasoning.
Key Responsibilities:
- Design and evaluate information access and reasoning strategies across RAG, agents, and classic ML approaches: chunking, embedding models, hybrid search, metadata filtering, semantic routing
- Prototype GenAI workflows (including agentic systems) that map and reason over compliance objects (controls, risks, requirements, evidence)
- Explore ML and probabilistic approaches where GenAI is not optimal: classifiers, ranking models, graph/link prediction, calibration, structured prediction
- 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:
- 6+ years of experience in applied research, data science, or ML with 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 significance
- Strong Python skills and comfort with notebook-driven research workflows
- Experience communicating research findings to engineering teams and translating insights into actionable improvements
- Bonus: Experience with compliance, legal, or document-heavy domains
- Bonus: Publications or contributions in IR, NLP, or RAG evaluation
