Fieldguide offers market-leading Artificial Intelligence that helps Advisory and Audit firms grow.
Built by Big Four practitioners and Silicon Valley technologists, our AI platform digitizes the…
Fieldguide offers market-leading Artificial Intelligence that helps Advisory and Audit firms grow.
Built by Big Four practitioners and Silicon Valley technologists, our AI platform digitizes the…
What they do: AI-native engagement platform that automates audit and advisory workflows (agentic Field Agents, document/workpaper management, request management, reporting)
Customers: Adopted by top CPA and advisory firms; marketing claims about broad Top 100 adoption
Founded: 2020
Founders / leaders: Jin Chang (CEO, co‑founder) and Chris Szymansky (CTO, co‑founder)
Recent funding: Announced $75M Series C (Feb 2, 2026) and prior $30M Series B (Mar 26, 2024); reported total funding ≈ $125M
Company Overview
Problem Domain
Audit, assurance, advisory, SOC, cybersecurity, and regulatory compliance workflows within CPA and consulting firms.
Founded
2020
Industry
Software Development
Funding Track Record
Series B- 2024-03-26
30000000
Announced to expand AI platform for CPA and advisory industry
Series C- 2026-02-02
75000000
Round announced with participation from new and existing investors
Investor Signal
“Backed by prominent Silicon Valley and strategic investors including Bessemer Venture Partners, 8VC, Floodgate, Y Combinator, Justin Kan, and others”
Founders
What we do
Join the Team
Artificial Intelligence Engineer
RemoteSan Francisco Bay Area, US
Remote • San Francisco Bay Area, US
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Who you are
You are an engineer who believes that evaluations are foundational to building reliable AI systems, not a nice-to-have
The following operating principles should resonate with you:
Evaluation-first mindset: You understand that for an AI company, not being able to evaluate a new model quickly is unacceptable
AI-native instincts: You treat LLMs, agents, and automation as fundamental building blocks and parts of the craft of engineering
Data-driven rigor: You make decisions based on metrics and are obsessed with measuring what matters
Production-oriented: You understand that evaluations must work on real production behavior, not just offline datasets
Strong product judgment: You can decide what matters and why, without waiting for guidance, not just how to implement it
Bias to building: You move fast and build working systems rather than perfect specifications
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)
Comfort operating in ambiguity and taking responsibility for outcomes
Deep empathy for professional-grade, mission-critical software (experience with audit and accounting workflows are not required)
What the job involves
Benefits
Health
Dental
PTO
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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
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
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
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
