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Fieldguide

Senior AI Engineer, Quality

San Francisco, CA or Remote (USA)Remote · United States
Senior · FullTime · Engineering, Product, and Design
POSTED 20 APRVERIFIED 5H AGO
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Role
203 days opentrue age
Posted 20 Apr
Payats field
$200k–250kposted
▼ −3% vs market median $233k
$200k$251k
Market p25–p75
$205k–$251k
SF Bay Area · n=149 · 365d to 6 Nov
CompanyFieldguide →
43open roles
Opened · 30d
+0
Closed · 30d
−0
3d of snapshots · 30d window
Market
1,501open · Senior ML / AI
Opened · 7d
+987% of open
All ml / ai
6,656open
All tracked boards · live 10 Nov · n=1,501
Visa
1H-1B LCAs · FY26
LCAs FY26 · n=1 · to 2026-04-23
LLMs

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.

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 a Senior 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.

Source · FieldguideFirst seen 2 NovOriginal ↗