Applied Research Engineer

Our Mission & Values

At Drata, we help companies earn and keep the trust of their users, customers, partners, and prospects. We’re the proof layer that shows great companies deserve the trust they aim to build.

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If you thrive when you’re empowered, energized, and working with smart, mission‑driven people, you’ll feel at home here.

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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.

• 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.

• 3+ years of experience in applied research, data science, or ML with a focus on NLP, information retrieval, or knowledge systems.
• 1+ 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.
• Exper…