Senior Applied AI Engineer

Drata is building the trust layer between great companies - automating compliance, managing risk, and helping organizations prove trust continuously as they scale. We’re Dratanauts: a global crew of 600+ professionals united by a culture that rewards integrity, ownership, and raising the bar, no matter where in the world we’re working from.

• Problems without a playbook: You’ll work at the edge of AI and security, building agentic governance, continuous compliance, and real-time trust verification to solve problems that don’t have an established answer yet. You’re writing it as you go.

• Real ownership, not just process: Our values center on owning outcomes and raising the bar, not checking boxes. You’re expected to have opinions and back them.

• A seat at the table: Your perspective is unique and valued. Open debate and diverse viewpoints are built into how decisions actually get made here, at every level.

• Growth at rocketship speed: Drata is scaling fast, which means scope grows fast too. High performers get more ownership, visibility, and experience.

• A crew, not just coworkers: Dratanauts consistently describe a "come as you are" culture with sharp, curious people—the kind of team that makes hard problems genuinely fun to solve. See what they say here and follow us on Linked In for company news, employee stories, and career updates.

Drata is seeking a Senior Applied AI 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.

• 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

• 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…