Applied Research Engineer
About The Position
Drata is seeking an 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.
Requirements
- 3+ years of experience in applied research, data science, or ML with a focus on NLP, information retrieval, or knowledge systems
- 1+ 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
Nice To Haves
- Experience with compliance, legal, or document-heavy domains
- Publications or contributions in IR, NLP, or RAG evaluation
Responsibilities
- 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
Benefits
- Stock equity
- Up to 100% employer-paid premiums for medical, dental, and vision coverage for employees and their dependents
- Comprehensive wellness benefits and healthcare concierge services
- 401(k) plan
- Company-paid life and disability insurance
- Tax-advantaged spending accounts
- Discounted voluntary offerings
- Paid Parental Leave policy
- Kindbody fertility and family-building benefits
- Dedicated leave specialists
- Generous annual stipends for both professional and personal development
- Access to a wide range of internal learning opportunities
- Flexible vacation policy
- Paid holidays
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
