Shiboleth automates lending compliance for financial institutions…. Backed by Y Combinator.
About the role
Agentic reperformance. Building agents that reconstruct what a statement, notice, or dispute resolution should have been from raw data — recalculating minimum payments, interest, and finance charges, then comparing against what the consumer actually received. Context engineering for regulation. Turning dense regulatory text and bank-specific test requirements into specifications an LLM-as-judge can apply consistently across populations of hundreds of thousands of records.
What they're looking for
- This is a founding engineer role, not a specialist one. We care more about how you think than which frameworks you've memorized. The people who thrive here usually:
- Reason from first principles. A physics, math, or competitive-programming background is a strong signal — not for the credential, but for the habit of decomposing an unfamiliar problem and testing hypotheses against reality
- Are comfortable being fuzzy. Compliance is ambiguous, our roadmap moves, and you'll context-switch across regulatory domains constantly. You're someone who can hold uncertainty and still ship
- Communicate exceptionally well. This is near the top of our list. You'll work directly with banks and translate between regulation, code, and customers — clear thinking on the page and out loud matters as much as clean code
- Own problems end-to-end. Customer obsession over ticket-closing. You don't hand off at the edge of your function, you follow the problem until the customer's outcome is right
- A fintech, banking, or consulting background is a plus — regulatory familiarity shortens the ramp. But what we're really hiring for is someone who finds it interesting that a $0.09 discrepancy on a minimum payment is a finding
More about this role
We're rebuilding bank compliance as an engineering problem. Today, auditors sample 5% of a fintech's files by hand and write findings in spreadsheets. We run the actual math on 100% of them — every dispute, every statement, every adverse-action notice — with deterministic rules and LLM-as-judge evaluation. You'll be one of the first engineers, building the agents and pipelines that reperform a bank's compliance at population scale. If you've shipped real LLM systems and want them to do something that matters, let's talk.
Before a fintech can issue a loan, send a statement, or resolve a dispute, its partner bank has to prove the whole operation followed the rules — Reg E, TILA, FCRA, SCRA, UDAAP, and dozens more. The way that's checked today hasn't changed in decades: consultants pull a small sample, eyeball it, and hope the other 95% looks the same.
It usually doesn't. On one Reg E engagement we reperformed the error-resolution math on 14,000+ disputes and surfaced timing and provisional-credit violations that no sample would have caught. On a TILA engagement we rebuilt daily balances and finance charges from raw platform data and found a systematic interest-method bug the bank...
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