Fieldguide

Status: researching

Priority: 2

Role: AI Engineer

Job ops data:

• ID: 7e084167-15c3-4d89-839d-5086090d0652.
• Score: 84.
• Status: ready.
• Source: linkedin.
• Salary: $150,000-$220,000.
• Location: San Francisco; described as remote-first, with exact office expectations unclear.
• Application: Open listing.
• App brief: build AI agents, retrieval logic, evaluations, and production feedback loops for enterprise audit and advisory workflows.
• App suitability reminder: strong match on product engineering, React/Postgres, workflow automation, AI systems, and cross-functional delivery.

Company thesis:

• Fieldguide automates assurance and audit work across cybersecurity, privacy, and financial audits.
• The role translates high-stakes customer workflows into reliable agent behavior, making it FDE-adjacent even though it sits in product engineering.

Fit for me:

• Strongest match: turning customer workflows into engineering requirements and shipped software.
• Second match: RAG, retrieval, human-in-the-loop systems, workflow automation, React, and Postgres.
• Third match: explainability, evaluation, and trust in professional workflows.
• Biggest gap: explicit production TypeScript/Python and direct ownership of deployed LLM evaluation systems.
• Risk or concern: the listing describes a typical candidate with 1-3 years of experience and senior guidance, which may indicate a level mismatch.

Relevant wiki links:

• RAG And Knowledge Systems
• AI Evaluation And Quality Measurement
• Customer-Facing AI Acceptance Test Plan
• Security Governance And Compliance

Signal to research:

• Fieldguide's audit workflow and primary practitioner personas.
• Whether remote-first includes permanent Colorado-based employees.
• How agent outputs are evaluated for explainability and professional judgment.
• Whether the company has a more senior applied-AI or product-engineering track.

Interview prep:

• Story to prepare: building a trustworthy workflow around sensitive or high-stakes information.
• Technical topic to refresh: retrieval evaluation, grounded outputs, human review, and audit trails.
• Customer scenario to practice: automate part of an audit workflow while keeping judgment, evidence, and approval visible.
• Smart question to ask: "How do you distinguish an agent output that is technically grounded from one an auditor can actually rely on?"
• Resume / cover letter angle: experienced workflow builder bringing product judgment, customer translation, and trustworthy AI patterns to professional services software.

Next action:

• Confirm role level and Colorado remote eligibility before investing in full application tailoring.

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