AI Engineer, Quality (Evals) at Fieldguide

About Us

Fieldguide is establishing a new state of trust for global commerce and capital markets through automating and streamlining the work of assurance and audit practitioners specifically within cybersecurity, privacy, and financial audit. Put simply, we build software for the people who enable trust between businesses.

We're based in San Francisco, CA, but built as a remote-first company that enables you to do your best work from anywhere. We're backed by top investors including Growth Equity at Goldman Sachs Alternatives, Bessemer Venture Partners, 8VC, Floodgate, Y Combinator, DNX Ventures, Global Founders Capital, Justin Kan, Elad Gil, and more.

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. We're backed by Bessemer Venture Partners, 8VC, Floodgate, Y Combinator, Elad Gil, and other top-tier investors.

As an AI Engineer, Quality, you will own the evaluation infrastructure that ensures our AI agents perform reliably at enterprise scale. This role is 100% focused on making evaluations a first-class engineering capability: building the unified platform, automated pipelines, and production feedback loops that let us evaluate any new model against all critical workflows within hours. You'll work at the intersection of ML engineering, observability, and quality assurance to ensure our agents meet the rigorous standards our customers demand.

We're hiring across all levels. We'll calibrate seniority during interviews based on your background and what you're looking to own. This role is for engineers who value in-person collaboration at our San Francisco, CA office.

What You'll Own

Measurable AI Agents

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

Rapid Model Evaluation

Build automated pipelines that evaluate new models against all critical workflows within hours of release

Design evaluation harnesses for our most complex Agentic systems and workflows

Implement comparison frameworks that measure effectiveness, consistency, latency, and cost across model versions

Design guardrails and monitoring systems that catch quality regressions before they reach customers

AI-native engineering execution

Use AI as core leverage in how you design, build, test, and iterate

Prototype quickly to resolve uncertainty, then harden systems for enterprise-grade reliability

Build evaluations, feedback mechanisms, and guardrails so agents improve over time

Work with SMEs and ML Engineers to create evaluation datasets by curating production traces.

Design prompts, retrieval pipelines, and agent orchestration systems that perform reliably at scale

Ownership of Quality and Large Product Areas

Define and document evaluation standards, best practices, and processes for the engineering organization

Advocate for evaluation-driven development and make it easy for the team to write and run evals

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

Experience

We care more about capability and trajectory than years on a resume, but most strong candidates will have:

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

Worked with vector databases, embedding models, and RAG architectures

Experience with evaluation platforms (LangSmith, Langfuse, or similar)

Compensation: $170K - $220K.