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Fieldguide

Builds AI software for audit and advisory firms

Senior AI Engineer - Quality

AI Application EngineeringConfirmed live in the last 24 hours
Full-time$200k - $250k /yr

About the Job

Requirements

  • Have multiple years of experience shipping production software in complex, real-world systems.
  • Have experience with TypeScript, React, Python, and Postgres.
  • Have built and deployed LLM-powered features serving production traffic.
  • Have implemented evaluation frameworks for model outputs and agent behaviors.
  • Have designed observability or tracing infrastructure for AI/ML systems.
  • Have worked with vector databases, embedding models, and retrieval-augmented generation architectures.
  • Have experience with evaluation platforms such as LangSmith, Langfuse, or similar.
  • Be comfortable operating in ambiguity and taking responsibility for outcomes.
  • Have empathy for professional-grade, mission-critical software; audit and accounting workflow experience is not required.

Responsibilities

  • Design and build a unified evaluation platform as the single source of truth for agentic systems and audit workflows.
  • Build observability systems that surface agent behavior, trace execution and production failure modes, and create feedback loops that turn production failures into 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 large language models, tools, retrieval systems, and logic into cohesive, reliable agent experiences.
  • Build automated pipelines that evaluate new models against critical workflows within hours of release.
  • Design evaluation harnesses for 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 a core leverage in designing, building, testing, and iterating.
  • 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 subject matter experts and machine learning 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 evaluations.
  • Partner with product and machine learning engineers to integrate evaluation requirements into agent development from day one.
  • Take full ownership of large product areas rather than executing on narrow tasks.

About the Company

Fieldguide

Builds AI software for audit and advisory firms

Fieldguide builds AI software for accounting and advisory firms to manage audits, risk assessments, and other client engagements in one shared workspace. Its Field Agents divide engagement work across planning, evidence testing, review, and financial statement preparation, with practitioners directing the work and approving results. The platform connects document requests, workpapers, and reporting so firms and their clients work from the same engagement records instead of separate files and email threads. Firms buy subscriptions to the hosted software, with implementation services covered by their contracts. Fieldguide was founded in 2020 and is headquartered in San Francisco, California, United States.

Company size
201-500
Company stage
Series C
Total funding
$125.1M
Headquarters
San Francisco, CA
Founded
2020

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