Staff AI Engineer

Title: Staff AI EngineerLocation: San Francisco, CA (Hybrid)Compensation: Up to $280,000 + EquityWe’re partnered with a high-growth, mission-driven SaaS company transforming how businesses build and maintain trust, with AI at the core of their next phase of innovation. The platform is redefining how critical enterprise workflows are automated, particularly in areas where reliability, auditability, and security are essential.This is a high-impact role where you will help define how AI is architected across the company. You won’t just be building features. You’ll make foundational decisions around systems, evaluation, and long-term technical direction, working across LLMs, retrieval systems, and agent-based workflows in production environments.What You’ll DoDesign and own production AI systems end-to-end, including LLM pipelines, retrieval systems, and orchestration layersBuild and scale RAG systems, reranking pipelines, and vector-based search infrastructureDefine evaluation frameworks to measure retrieval quality, reasoning accuracy, and system performanceAnalyze production behavior, identify failure modes, and drive improvements based on dataMake key architectural decisions across model infrastructure, tooling, and workflowsPartner closely with product, platform, and domain teams to translate complex requirements into scalable systemsLead best practices for building reliable, observable, and cost-efficient AI systemsRequirements10+ years of software engineering experience, including 3+ years working on ML or AI systemsProven experience owning and deploying production LLM systemsStrong background in RAG, embeddings, reranking, and vector databases (e.g., Pinecone, FAISS, Chroma)Experience designing evaluation systems and improving models through quantitative analysisStrong Python skills, with solid software engineering fundamentalsExperience making architectural decisions that influence team or org directionStrong understanding of production systems, including reliability, observability, and cost tradeoffsAbility to break down ambiguous problems and operate with a high degree of ownershipClear communication skills and experience working cross-functionallyNice to HaveExperience in regulated domains such as compliance or securityFamiliarity with data platforms or analytics toolingExperience with orchestration frameworks (e.g., Temporal, Airflow)Exposure to LLM evaluation platforms or toolingContributions to open source, research, or technical communitiesIf you're interested in shaping how AI systems are built, evaluated, and deployed in high-trust environments, this is an opportunity to have direct influence on both technical direction and real-world impact at a fast-growing company.