domain

Principal Software Engineer, AI

US Dallas, TX, US
Onsite 2026-07-01
Announced salary
$200,000 - $300,000
Low
$88K
Median
$123K
High
$158K
Market in Dallas · BLS OEWS 2025
Estimated net pay
$12,411 - $17,931
/month · 26% withheld
after tax & contributions · Single, no dependents

Job description

**About Pepper Auditors** Pepper's mission is to protect the integrity of every American's employee benefit plan. We are a fast\-growing team of technologists and CPAs working to build a modern audit firm that can serve as the fulfillment engine for federally mandated compliance work streams. **The Role** We’re looking for an experienced software engineer to help build and scale our audit workbench; a pipeline\-based harness — not a generic agent loop — that takes an engagement from raw evidence to reviewable workpaper. As of today that looks roughly like: * Ingestion and extraction * Recomputation and exception detection * Workpaper generation * Eval infrastrcuture * Frontend review surface As one of the first engineering hires, you will help set the technical direction to ensure our audit wokrbench is usable and scalable from our our first 100 clients to our next 10,000\. **The hard part** Most "build an AI agent" roles optimize for demo velocity. This one optimizes for a number a CPA stakes their license on. The reliability of this system comes from schema design, deterministic rule logic, validation, and structured human review — **not** from hoping the model is good. You'll spend as much time on the deterministic\-versus\-stochastic boundary, eval harnesses, and traceability as on prompts and orchestration. The model does extraction and narrative drafting; the math and the regulatory logic are exact code. If "build something auditable, reproducible, and provably correct in a regulated domain" is more interesting to you than "ship a chatbot," you'll like it here. **You're a strong fit if you** * Have shipped **production LLM systems where correctness mattered** — extraction pipelines, agentic workflows, or document\-understanding systems — ideally in a domain with real consequences (fintech, legal, healthcare, regtech). * **Build evals as a reflex.** You've maintained golden datasets and caught regressions before they reached users, and you treat "w

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