Senior Applied Research Engineer 2
About The Position
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.
Requirements
- 6+ 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
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 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
Senior
Education Level
No Education Listed
