Title : AI Engineer
Job Type : Full Time
Location : San Francisco, CA
Salary : $170K/yr – $220K/yr

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 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.

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.
Design prompts, retrieval pipelines, and agent orchestration systems that perform reliably at scale

Ownership of Quality and Large Product Areas

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

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

Experience

We care more about capability and trajectory than years on a resume, but most strong candidates will have:

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 Should Excite You

Agent reliability at enterprise scale: Building systems that professionals depend on
Balancing automation with human oversight: Knowing when to automate and when to surface decisions to experts
Production feedback loops: Turning real-world agent failures into systematic improvements
Explaining AI decisions: Making all forms of AI outputs and agent reasoning transparent and trustworthy
Evaluation for nuanced domains: Structuring data and feedback for workflows where ground truth requires expert judgment
High-impact visibility: Your work directly enables leadership to confidently communicate AI quality to the board and customers