Staff Applied Research Engineer

Drata • San Francisco, CA

1d • $220,800 - $298,800 • Hybrid

About The Position

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. Drata's 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.

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
• Experience with RAG systems: chunking strategies, vector databases, retrieval optimization
• 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

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, 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

Benefits

• Stock equity
• Up to 100% employer-paid premiums for medical, dental, and vision coverage for employees and their dependents
• Comprehensive wellness benefits
• Healthcare concierge services
• 401(k) plan
• Company-paid life and disability insurance
• Tax-advantaged spending accounts
• Discounted voluntary offerings
• Paid Parental Leave policy
• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed