--- title: "SOX Audit Expert" description: "Engagement: 2 weeks · 8 hours/week Compensation: Competitive hourly rate About Us: At Nace AI, we are redefining how professional services operate by deliver…" canonical: "https://nace.ai/careers/4a3a7ab4-0a86-49a4-aa80-8df4b9f2ab3d" updated: 2026-03-27 --- # SOX Audit Expert > Engagement: 2 weeks · 8 hours/week Compensation: Competitive hourly rate About Us: At Nace AI, we are redefining how professional services operate by deliver… Finance & Audit · Palo Alto, CA · Contract [Apply for this position](https://jobs.ashbyhq.com/nace.ai/4a3a7ab4-0a86-49a4-aa80-8df4b9f2ab3d/application) Engagement: 2 weeks · 8 hours/week Compensation: Competitive hourly rate About Us: At Nace AI, we are redefining how professional services operate by delivering Sovereign Specialized Intelligence. As an applied research and product company, we equip enterprises with a comprehensive AI stack to build customized, secure intelligence tailored to their unique business needs. Driven by advanced Small Language Models and our dynamic metamodel framework, our flagship platforms - Nace Data Intelligence and the Nace SLM Cloud - enable true end-to-end business process automation. The result is transformative ROI: professional services firms using Nace AI are currently recovering 1,000 hours per client engagement, drastically reducing overhead and accelerating delivery. The work we are doing has a meaningful impact across industries, and every hire at Nace AI plays a critical role in shaping the company’s trajectory. This is a unique opportunity to join a high conviction AI company at an early stage and directly influence its growth. If building a world-class AI team from the ground up excites you, we’d love to talk. THE OPPORTUNITY We are building an AI-native platform for SOX control testing — one where an AI agent reads an audit workpaper, classifies every sheet, extracts sample rows, identifies exceptions, and generates PCAOB-defensible narratives. Automatically. The engineering is working. What it needs now is an expert who can tell us when it's right and when it only looks right. This is not a training role. This is not a data labeling role. This is the role where a seasoned auditor looks at what the AI produced, applies the kind of judgment that comes from years of real engagements, and tells us exactly where professional standards would push back — and why. Your input directly shapes how AI handles SOX compliance for public companies. That influence starts on day one. What You Will Actually Do Review AI-generated workpaper interpretations against real audit standards. The system parses uploaded Excel workpapers and makes decisions: which tab is the testing table, which rows are samples, which variance is an exception. You will tell us when those decisions are audit-defensible and when they are not. Define ground truth across control types. Reconciliation controls, management review controls, IT access reviews, three-way match, segregation of duties — each behaves differently. You will tell us what a correct result looks like for each, and provide real workpaper examples where possible. Challenge the AI's exception logic. When the system flags a variance as an exception, is it? When it concludes a control is effective with limited samples tested, should it? You provide the professional judgment the engineering team cannot. Shape the product roadmap. You will identify gaps between what auditors actually need and what the system currently produces. Those gaps become the next build cycle. WHO THIS IS FOR You have 5+ years of hands-on SOX audit experience, including direct workpaper preparation and review — not just oversight. You know what a PCAOB reviewer looks for because you have been in that room. You came up through a Big 4 or equivalent top-tier firm (EY, PwC, Deloitte, KPMG, or a national practice with equivalent SOX depth). You hold a CPA and have worked across multiple clients and control environments, not just one industry. You are intellectually curious about what AI gets wrong — not defensive about it. You see the potential for AI in audit and want to be part of getting it right from the beginning, before the standards catch up. Minimum Qualifications - 5+ years SOX compliance and financial audit experience, hands-on - Big 4 or equivalent top-tier firm background required - Deep working knowledge of PCAOB standards and COSO framework - CPA certification required - Experience spanning multiple control types, not single-domain PREFERRED - Experience leading SOX workstreams for mid-to-large cap public companies - Former senior associate or manager level — you reviewed others' workpapers - Exposure to control testing automation or GRC tools - Intellectual interest in where AI belongs — and doesn't belong — in regulated workflows [Apply for this position](https://jobs.ashbyhq.com/nace.ai/4a3a7ab4-0a86-49a4-aa80-8df4b9f2ab3d/application) ## For agents - [Drex API guide](https://nace.ai/drex.md): the decision model: request shape, question types, benchmarks and deployment, as markdown - [Decision endpoint](https://console.nace.ai/v1/systemone): POST with model "drex-latest", a state string and typed questions (choice, noul, score); https://console.nace.ai/v1/models lists checkpoints - [Document endpoints](https://console.nace.ai/v1/documents/{parse|split|classify|extract|ground}): POST a source (a public URL, an uploaded workspace file or a finished parse job) and poll GET https://console.nace.ai/v1/documents/jobs/{id}; the same key and credit as decisions - [Console](https://console.nace.ai): API keys, the playground, usage and billing; send `Authorization: Bearer $NACE_API_KEY` on every request - [Console docs](https://console.nace.ai/docs): quickstart, question types, the document routes, errors, limits and pricing - [Nace Document Intelligence](https://nace.ai/perception.md): the Perception series: parse, split, classify, extract and ground any file type with a location on every value - [Drex for Search](https://nace.ai/drex/search.md): Drex for Search: decision-powered retrieval for agents, Drex finds the page, cited search through the CLI, SDKs, MCP and REST, and the public benchmark ranks - [Use cases](https://nace.ai/use-cases.md): every Drex use case with its proof figures: browser use and search for developers, enterprise finance teams and professional firms - [Developers](https://nace.ai/developers.md): The execution layer for agents: Perception, Decision and Search behind one key, the Drex Max frontier, install steps, a scripted agent session and the four deployment boundaries - [Site index](https://nace.ai/llms.txt): every page on nace.ai; append `.md` to any page URL or send `Accept: text/markdown` for the markdown version