AI Engineer, Quality

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.

We value diversity — in backgrounds and in experiences. We need people from all backgrounds and walks of life to help build the future of audit and advisory. Fieldguide’s team is inclusive, driven, humble and supportive. We are deliberate and self‑reflective about the kind of team and culture that we are building, seeking teammates that are not only strong in their own aptitudes but care deeply about supporting each other's growth.

As an early stage start‑up employee, you’ll have the opportunity to build out the future of business trust. We make audit practitioners’ lives easier by bringing together up to 50% of their work and giving them better work‑life balance. If you share our values and enthusiasm for building a great culture and product, you will find a home at Fieldguide.

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 creating evaluations as 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.

• 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 Lang Smith and Lang Graph

• Translate customer problems into concrete agent behaviors and workflows

• Integrate and orchestrate LLMs, tools, retrieval systems, and logic into cohesive, reliable agent experiences

• 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

• 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