Applied AI Engineer | Finance

Design and ship the production AI systems that power how consumer brands manage their financial operations. You'll work at the intersection of LLMs, financial data, and the many integrations that underpin Iris.

About us

Our mission at Iris is to build the next great financial firm for the consumer economy.

We are building the AI-native financial operating system which every modern brand will be run on. Our customers include well known, fast-growing brands reaching hundreds of millions of dollars in revenue and PE-backed, legacy businesses looking to bring their operations into the AI world.

We’ve raised over $8M to date and have scaled to millions of dollars in revenue.

📍 This position is remote-friendly, but we have New York and Chicago hubs.

Who we need:

As an AI Engineer at Iris, you'll design and ship the production AI systems that power how consumer brands manage their financial operations. You'll work at the intersection of LLMs, financial data, and the many integrations that underpin Iris.

We're looking specifically for engineers who have built AI applications where financial data was the core domain. You’ve worked with financial data specifically: income statements, bank transaction feeds, payroll records, inventory data, channel-level revenue, accounts receivable and payable, or similar. If you've built an AI application on top of accounting exports, financial APIs, or ERP data and had to grapple with its messiness, you know exactly what this role is.

You'll own high-impact systems end-to-end, from model integration to production infrastructure. Your work will be used daily by the finance teams and operators running some of the fastest-growing consumer brands in the country.

Key Responsibilities:

1. Own AI features end-to-end: from designing agentic workflows and RAG pipelines that power our agents to the infrastructure that runs them reliably in production at scale.

2. Work on genuinely hard problems: financial data is messy, high-stakes, and unforgiving. A P&L spanning DTC, Amazon, retail, and B2B; inventory crossing multiple 3PL partners; a bank feed reconciled against many payment processors.

3. Build the evaluation frameworks and experimentation loops that turn plausible-looking AI outputs into reliable, auditable, financially accurate ones. Hallucinations in finance are expensive.

4. Partner directly with our CTO and domain experts to push the frontier of what AI can do inside a financial operating system.

Qualifications:

• 2+ years in a technical role with a strong foundation in backend systems, APIs, and data engineering
• 2+ years shipping production AI systems with real users
• Direct experience building AI applications where financial data was the core input: QuickBooks or NetSuite exports, bank transaction feeds, Shopify or Amazon revenue data, payroll records, inventory data, or similar.
• Experience with production LLM applications: RAG pipelines, agentic systems, structured extraction from financial documents
• Python proficiency
• Some financial literacy

Compensation:

• Competitive salary, commensurate with experience.
• Significant equity package, based on experience.

Interview process

• Intro conversation with our CTO.
• Technical screen.
• Take-home or live session focused on a real financial AI problem (expect to get into the weeds on how you'd approach financial data for LLM applications).
• Onsite to meet our engineering team and leadership in New York or Chicago.
• References (we check references on everyone at Iris — it's how we keep the bar high).

Please send your resume and interest to jobs@irisfinance.co