Machine Learning Engineer

Job Summary 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 research‑focused role emphasizes experimentation and rigor over production engineering. You will own the science behind how Drata’s AI products retrieve, reason, and respond—and you will work closely with AI and Software Engineers to turn validated approaches into production‑ready systems. What You’ll Do 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 What You’ll Bring 5+ 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 Bonus: Experience with compliance, legal, or document‑heavy domains Bonus: Publications or contributions in IR, NLP, or RAG evaluation How We Support You Shared Success: Stock equity gives employees ownership and a share of company growth Health & Wellness: Up to 100% employer‑paid premiums for medical, dental, and vision coverage for employees and dependents, along with comprehensive wellness benefits and healthcare concierge services Financial Well‑being: 401(k) plan, company‑paid life and disability insurance, tax‑advantaged spending accounts, and discounted voluntary offerings Family Support: Paid parental leave after six months, access to fertility benefits and leave specialists Growth & Development: Generous annual stipends for professional and personal development, and access to internal learning opportunities Time Off & Flexibility: Flexible vacation policy, paid holidays, and other perks to recharge Salary & Benefits 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: $166,900 - $225,900. A variety of factors are considered when determining leveling and compensation—these ranges may be modified in the future and final offers may vary. #J-18808-Ljbffr