Staff 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. Why Join the Drata Team? At Drata, you're not maintaining legacy compliance software - you're building the agentic AI platform defining what trust looks like for the next generation of companies. Here's what makes the work itself worth showing up for: - 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 https://drata.com/about/careers/life and follow us on LinkedIn https://www.linkedin.com/company/drata/posts/?feedView=all for company news, employee stories, and career updates. Job Summary: At Drata, we’re rethinking how compliance gets done, and AI is a core part of that. We’re looking for a Staff AI Engineer to help shape how intelligent systems power trust-critical enterprise workflows. This isn’t a “build a feature and move on” role. You’ll help define how AI is architected across the company. You’ll make foundational decisions about what we build, how we measure quality, and where we invest. From early research to production deployment, you’ll own systems end-to-end, and your judgment will directly influence our technical direction. You’ll work at the intersection of LLMs, retrieval systems, agentic workflows, and real-world compliance challenges, partnering closely with product, platform, and security teams to build AI that’s powerful, reliable, and responsible. What you’ll Do: SHAPE THE ARCHITECTURE - Design and own production AI systems end-to-end (LLM pipelines, RAG, reranking, vector stores, orchestration). - Make thoughtful build/don’t-build decisions based on real data. - Evolve our AI stack over time, from model infrastructure to workflow orchestration to evaluation tooling. RAISE THE QUALITY BAR - Design evaluation systems that measure retrieval quality, reasoning accuracy, end-to-end performance. - Build tooling that helps the team iterate confidently and catch regressions early. - Define how we measure success across the platform. INVESTIGATE DEEPLY & DECIDE WITH EVIDENCE - Analyze production outputs to identify failure patterns and root causes. - Turn complex findings into clear technical recommendations. - Know when to push forward and when to pause based on data. LEAD ACROSS TEAMS - Be the go-to technical voice for AI architecture decisions. - Influence standards for how LLM systems are built, tested, deployed, and monitored. - Mentor senior engineers through design reviews and hands-on collaboration. - Partner with product and compliance teams to translate domain complexity into clean technical solutions. BUILD RESPONSIBLE, PRODUCTION-READY AI - Ship systems optimized for latency, cost, reliability, and auditability. - Embed safety guardrails, confidence thresholds, and human-in-the-loop workflows. - Ensure outputs are traceable and explainable. What you’ll bring: - 10+ years of software engineering experience, including 3+ years working directly on ML/AI systems. - Real ownership of production LLM systems. - Deep experience with RAG, embeddings, reranking, vector databases (Pinecone, FAISS, Chroma, etc.), and agentic workflows. - Experience designing evaluation frameworks and using quantitative analysis to improve system performance. - Strong Python skills (TypeScript is a plus). - A track record of making architectural decisions that shape team direction. - Experience operating AI systems in production - observability, reliability, cost tradeoffs. - The ability to break down ambiguous, high-stakes problems into structured investigations. - Clear communication skills and comfort working cross-functionally. Nice to have: - Experience in compliance, security, or other regulated domains. - Familiarity with enterprise data platforms or Snowflake-based analytics. - Experience