drata - Senior Applied AI Engineer
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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 analysis • Strong Python skills and comfort with notebook-driven research workflows • Experience communicating research findings to engineering teams and translating insights into actionable recommendations • Bonus: Experience with compliance, legal, or document-heavy domains • Bonus: Publications or contributions in IR, NLP, or RAG evaluation • Your contributions at Drata will not only shape the future of compliance and security automation but also solidify our standing as an innovator in the space, driving forward our mission with cutting-edge technology. • How we support you: • At Drata, our people are our strongest advantage—and we prove it with support that exceeds industry standards. Our total rewards package is designed to power your well-being, accelerate your growth, and keep your work-life balance thriving. • Explore how we invest in your Life at Drata https://drata.com/about/life-at-drata?utm_source=chatgpt.com.
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
• The best way to understand the Driver’s Mindset is to see it in action. We’re an award-winning, mission-driven team of 600+ people worldwide, united by a culture that values trust, speed, and continuous growth. • See the Speed: https://www.youtube.com/watch?v=QidTdkGwKMY Watch our CEO, Adam Markowitz, discuss the hyper-growth journey, from $0 to $100M ARR in just four years • Hear the Voice of the Team https://drata.com/about/life-at-drata: Explore our "Life at Drata" page for employee testimonials on our collaborative and the growth opportunities available. • Experience the Impact https://www.greatplacetowork.com/certified-company/7044563: See why we are consistently recognized on Fortune's Best Workplaces lists. • Connect with Us on Socials: LinkedIn https://www.linkedin.com/company/drata/posts/?feedView=all - follow us for company updates, employee stories, and career news.
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