Senior AI Engineer, Quality
Fieldguide · San Francisco, CA · 1 mo ago
HybridEngineering$200k–$250k/yrFull-time
About the role
Fieldguide is building AI agents for the most complex audit and advisory workflows. We're a San Francisco-based Vertical AI company building in a $100B+ market undergoing rapid transformation. Over 50 of the top 100 accounting and consulting firms trust us to power their most mission-critical work.
Responsibilities
- Design and build a unified evaluation platform that serves as the single source of truth for all of our agentic systems and audit workflows
- Build observability systems that surface agent behavior, trace execution, and failure modes in production, and feedback loops that turn production failures into first-class evaluation cases
- Own the evaluation infrastructure stack including integration with LangSmith and LangGraph
- Translate customer problems into concrete agent behaviors and workflows
- Integrate and orchestrate LLMs, tools, retrieval systems, and logic into cohesive, reliable agent experiences
- Rapidly build and deploy LLM-powered features serving production traffic
- Build evaluation frameworks for model outputs and agent behaviors
- Design guardrails and monitoring systems that catch quality regressions before they reach customers
- Define and document evaluation standards, best practices, and processes for the engineering organization
- Partner with product and ML engineers to integrate evaluation requirements into agent development from day one
- Take full ownership of large product areas rather than executing on narrow tasks
Qualifications
- Multiple years of experience shipping production software in complex, real-world systems
- Experience with TypeScript, React, Python, and Postgres
- Built and deployed LLM-powered features serving production traffic
- Implemented evaluation frameworks for model outputs and agent behaviors
- Designed observability or tracing infrastructure for AI/ML systems
- Experience with evaluation platforms (LangSmith, Langfuse, or similar)
- Comfort operating in ambiguity and taking responsibility for outcomes
- Deep empathy for professional-grade, mission-critical software