Staff Applied Research Engineer

Job Summary

Drata, at the vanguard of compliance software innovation and renowned for its commitment to trust and security across the internet, is on an ambitious path to redefine how AI and General AI technologies bolster compliance automation. Drata is seeking an Applied AI Engineer to drive the quality and effectiveness of our AI systems through rigorous research, experimentation, and evaluation. In this role, you will optimize retrieval strategies, build evaluation frameworks, and establish the scientific foundation that enables our AI features to deliver accurate, trustworthy results. This is a research-focused role emphasizing experimentation and rigor over production engineering. You'll work closely with AI Engineers handing off validated approaches for them to productionize while owning the quality metrics and evaluation systems that ensure our AI delivers on its promises.

About the Role

The Drata compliance platform is document-heavy: VRM Agent, AIQA, Trust Agent, policy-to-control mappers, and regulatory summarization all depend on retrieving the right information from large document sets. Your work will directly impact how well our AI understands and navigates compliance artifacts.

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
• 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

Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or related field
• Experience with RAG systems: chunking strategies, vector databases, retrieval optimization
• Publications or contributions in IR, NLP, or RAG evaluation

Skills

• Applied Research
• Data Science
• Machine Learning
• Natural Language Processing
• Information Retrieval
• Knowledge Systems

Benefits

• Stock Equity
• Health & Wellness
• Financial Well-being
• Family Support
• Growth & Development
• Time Off & Flexibility

Pay

A competitive base salary, benefits, and stock, typically in the form of Restricted Stock Units (RSUs). The applicable salary range for this role is: $220,800 - $298,800.

Schedule

Flexible schedule with high-impact collaboration days in the Bay area (Tuesday through Thursday) and flexible Mondays and Fridays for focused work, balance, and autonomy.