About Us

Fieldguide is establishing a new state of trust for global commerce and capital markets by automating and streamlining the work of assurance and audit practitioners — specifically in cybersecurity, privacy, and financial audits. We build software for the people who enable trust between businesses.

We're based in San Francisco, CA, and we're backed by Goldman Sachs Alternatives, Bessemer Venture Partners, 8VC, Floodgate, Y Combinator, and more. Over 50 of the top 100 accounting and consulting firms trust Fieldguide to power mission-critical work.

About the Role

You'll join a genuine 0→1 team on the ground floor of one of the company's biggest new bets. This seat is specifically product-focused: you'll own agent quality, ship agents that do real audit work, and work alongside practitioners.

Depending on your experience and what you're looking to own, you may join building within a major agent area, owning one end-to-end, or setting technical direction for agentic audit work across the team. We're hiring across all levels and will calibrate during interviews based on scope and demonstrated experience.

What You'll Do

Make agent judgment repeatable: run error analysis on real testing data and turn findings into concrete fixes

Tradeoffs such as quality/latency/cost across a long multi-phase run

Build structured-output pipelines that turn model output into real audit artifacts

Take ambiguous problem statements and turn them into a plan, a shipped feature, and a clear read on what was cut and why

Work directly with an embedded subject matter expert and with design-partner firms, turning their feedback into agent changes within days

Expand agent coverage into new controls and new areas of internal audit

Who You Are (All Levels)

Product-minded and full-stack: you've shipped LLM-backed features to production against real users, and you measure yourself on whether they got used

You're fluent in evals and error analysis, and you apply them in service of shipping something practitioners trust

You have real opinions on model selection, prompting, and orchestration tradeoffs, and you can defend them with evidence rather than vibes

Energized by 0→1 work: you'd rather define the problem than inherit a spec, and you don't stall on ambiguity

Strong instincts for human-in-the-loop design

A genuine team player across the organization, not just within engineering: you'll work daily with PM, design, domain experts, and customer-facing teams, and you treat that as the best part of the job

Ship fast without leaving a mess: your code is reviewable, tested where it counts, and instrumented

Able to internalize a hard domain fast. You don't need to know SOX today, but you'll understand it well enough to make the right product calls

Higher-Level Responsibilities

At the Senior level, you may:

Own a major agent area end-to-end, from how the agent reasons about a class of controls through to the artifact a reviewer signs

Set the evals and error-analysis practice for the team's agent work, and decide what evidence justifies shipping a change or rolling it back

Collaborate with PMs and designers to shape roadmaps and define architectural tradeoffs, including where the agent acts and where the auditor decides

Own the harder model and orchestration judgment calls across a long multi-phase run

Mentor other engineers and raise the bar on 0→1 execution and applied eval rigor

At the Staff level, you may:

Drive agent initiatives that reach beyond Internal Audit and influence how agents are built across Fieldguide

Set and champion engineering standards for agent reliability, reproducibility, and defensibility

Partner with engineering and product leadership to define long-term technical strategy for agentic audit work

Serve as a trusted advisor to leaders across Engineering, Product, and Design

Represent Fieldguide externally through writing, speaking, and open-source contributions

Experience

Must-have

Shipped LLM-backed product features to production against real users

Applied AI skillset: evals, error analysis, and model-selection decisions you owned and can explain