США⚡️ Founding Full Stack Software Engineer ⚡️
1 мес. назад
170k–220k USD / годСШАSenior
llm
Founding Full Stack Software Engineer building systems to verify bank compliance at scale using LLM and engineering methods.
Требования
- 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.
- Are product-minded. Backend depth in Python (we run Django) and experience shipping real LLM systems — RAG, agentic workflows, evaluation harnesses — are genuinely useful here. But product judgment beats stack mastery, and a security background, while interesting, isn't required.
- 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.
- We believe regulatory compliance shouldn't be a bottleneck on financial innovation, and that the way you earn a bank's trust is by showing your work — both of which shape how we build.
Другое
- 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 had been disclosing incorrectly for months.
- That's the wedge: compliance testing is a verification problem, and verification problems are engineering problems. We test everything, we show our work, and we hand banks evidence an examiner will accept.
- Esty (CEO) dropped out of NYU's CS program at 19 and taught herself security well enough to earn the CISSP and OSCP. She built AI-based security systems at Invoca and led R&D at Buggy before this, and she still approaches compliance the way she approached pentesting — assume the system is broken and go find where.
- Bivu (CTO) spent a decade in ML engineering, including leading IVR fraud detection at Pindrop — a system that six of the ten largest U.S. banks now run in production.
- started as a deepfake-audio-detection research project that ended up on stage at Black Hat and then in TechCrunch. The through-line we kept hitting: security had matured into a real engineering discipline with continuous monitoring and standards, and compliance — a bigger, more regulated market — had almost none of that. We started to close that gap.
- 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.
- Population-scale pipelines. Ingestion, normalization, and PII redaction over messy SFTP/PGP feeds and warehouse data, feeding deterministic SQL rules and model-based evaluation.
- Evidence, not vibes. The hard part isn't the verdict — it's assembling defensible, examiner-ready evidence for every one of those verdicts.
- Month 1: Ship your first end-to-end rule suite for a live partner bank — owning it from raw data ingestion through evidence output, in production, with a real customer reading the results.
- Month 2: Take ownership of a piece of core platform — likely the agentic reperformance layer or the LLM-as-judge evaluation harness — and improve its accuracy and consistency on a domain you hadn't seen before.
- Month 3: Lead a new regulatory domain end-to-end (we're actively expanding into new test targets), proving the engine generalizes beyond what one person could have hand-coded.
- We ship to production daily. You'll have customer-facing impact in week one, not quarter two.
- San Francisco, hybrid. We value in-person collaboration but aren't dogmatic about a 5-day mandate.
- Founding-level equity and salary.
- We can't sponsor visas right now, and we're hiring in U.S. time zones.
- If this sounds like your kind of problem, reach out — a short note about something hard you've shipped goes a long way.