СШАFounding Engineer
3 нед. назад
100k–160k USD / годСШАSenior
llmdocument processingautomationcompliance
Develop and enhance a transaction and automation platform for alternative investments, focusing on document processing and regulatory compliance.
Условия
- Location: NYC
- Compensation: Competitive salary + meaningful equity. We're Series A — the equity is early enough to matter.
- Stack: Python, Angular/React, Express, PostgreSQL, AWS. But we care more about engineering judgment than stack familiarity.
Другое
- The alternative investment industry moves trillions of dollars a year through fax machines, PDFs, and manual data entry. A single subscription into a private equity fund can require 40+ pages of documents, hand-keyed across three different systems, touched by five different people, and take weeks to settle. The infrastructure is decades behind public markets — and the entire wealth management industry knows it.
- is fixing this. We're the transaction and automation platform for alternative investments, used by some of the largest RIAs, broker-dealers, and asset managers in the country — including Blackstone, Apollo, Blue Owl, and JLL. We're on track to process $5B in new transactions in 2025. We're a YC-backed company with Series A funding.
- This is not a clean-sheet build. We operate a production platform handling real money for institutional clients. The interesting engineering problems come from that constraint — you have to build fast and get it right, because the cost of getting it wrong is measured in regulatory risk and real dollars.
- Here's what you'd actually be working on:
- Agentic document systems. We're building LLM-powered pipelines that ingest, classify, extract, and generate complex financial documents — subscription agreements, capital call notices, K-1s. The challenge isn't getting a demo working. It's getting 99%+ accuracy on documents where a single wrong field is a compliance violation, across hundreds of fund-specific variations, with a human-in-the-loop review system that's fast enough to be useful.
- Automation that replaces manual operations. Our clients' workflows are deeply manual. We're systematically identifying high-volume, high-cost operational processes and building systems that eliminate them — not by shipping a chatbot, but by building structured automation with robust error handling, audit trails, and graceful fallbacks. Every automation we ship directly reduces burn and increases margin.
- Platform modernization under load. We inherited a large microservices codebase. We're migrating toward a modern, agent-friendly architecture while keeping production stable for clients processing millions in transactions. This is systems engineering, not greenfield hacking — and it requires judgment about what to rebuild, what to wrap, and what to leave alone.
- You report to the CTO. The team is small. That means your work has disproportionate impact, but it also means there's nowhere to hide. You'll own entire systems end-to-end: scoping, building, testing, deploying, and operating them in production.
- In your first 90 days, you'll likely:
- Ship production code in your first week
- Own a critical automation pipeline that has direct, measurable impact on the business
- Work directly with our operations and client service teams to understand the manual processes you're replacing — and build the thing that actually solves their problem, not the thing that looks good in a demo
- Make architectural decisions that the rest of the team builds on
- We're not looking for someone who needs a product spec and a Jira ticket to start working. We need someone who can look at a messy operational process, understand why it exists, figure out what "solved" looks like, and build it — then measure whether it actually worked.
- You've built and shipped systems that handle real money, real data, or real compliance constraints — not just prototypes
- You treat testing, validation, and edge case handling as part of the work, not a separate phase someone else does
- You've operated in an environment where you had to figure out the "what" and the "how," not just execute against a spec
- You're comfortable working across backend systems, data pipelines, and infrastructure — and you can pick up new tools and frameworks fast when the problem demands it
- You've worked with or built LLM-powered applications and understand both the potential and the failure modes
- You'd rather own a hard problem at a small company than be a cog in a machine at a big one
- Engineers who optimize for clean architecture over shipping. We care about code quality, but we care about outcomes more.
- People who need extensive onboarding before they're productive. Our codebase is messy in places. You need to be comfortable navigating ambiguity and building context on your own.
- Anyone who treats QA or validation as someone else's job. If you build it, you own whether it works — end to end, in production, with real users.