Senior Applied Research Engineer

Drata is seeking a Senior Applied Research Engineer to drive the quality and effectiveness of our AI systems through rigorous experimentation, evaluation, and applied research.

This is a research-focused role emphasizing experimentation and rigor over production engineering. You'll own the science behind how Drata's AI products retrieve, reason, and respond — and you'll work closely with AI and Software Engineers to turn validated approaches into production-ready systems.

Drata's compliance platform is document-heavy: VRM Agent, AIQA, Trust Agent, policy-to-control mappers, and more all depend on high-quality information retrieval and reasoning.

- Design and evaluate information access + reasoning strategies across RAG, agents, and classic ML: 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 + probabilistic approaches where GenAI is not the best fit: classifiers, ranking models, graph/link prediction, calibration, and 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

- 5+ 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 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

This role will receive a competitive base salary, benefits, and stock, typically in the form of Restricted Stock Units (RSUs). The applicable salary range for this role is: $166,900 - $225,900.