Applied Research Engineer

Location Hybrid - San Francisco Employment Type Full time Department Engineering Job Summary 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. 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 Qualifications 3+ years of experience in applied research, data science, or ML with a focus on NLP, information retrieval, or knowledge systems 1+ year 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 Benefits Shared Success : We provide stock equity to ensure that as the company grows, you share directly in that success. Health & Wellness : Up to 100% employer‑paid premiums for medical, dental, and vision coverage for employees and their dependents, along with comprehensive wellness benefits and healthcare concierge services. Financial Well‑being : A comprehensive suite of financial benefits, including a 401(k) plan, company‑paid life and disability insurance, tax‑advantaged spending accounts, and a range of discounted voluntary offerings. Family Support : Paid parental leave policy after six months of employment; access to fertility and family‑building benefits and dedicated leave specialists. Growth & Development : Generous annual stipends for both professional and personal development, internal learning opportunities. Time Off & Flexibility : Flexible vacation policy, paid holidays, and other perks to recharge. Compensation 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: $145,200 - $196,400. A variety of factors are considered when determining someone’s leveling and compensation–including a candidate’s professional background and experience. These ranges may be modified in the future and final offer amounts may vary from the amounts listed above. #J-18808-Ljbffr