Founding Engineer

ABOUT HYPERLOOP

Were building the Finance OS for the office of the CFO. Our agents run accounts receivable end to end — chasing invoices, answering payer questions, resolving disputes, applying cash. Autonomously, in production today, against real ledgers.

Enterprise, mid-market and self-serve, on one platform. Live with paying customers on signed annual contracts.

THE ROLE

Founding Engineer. Full-stack, leaning hard into ML. Youd own the product surface and the agent layer underneath it, with real authority over the architecture and where it goes next.

WHY WE NEED YOU

Youve probably been the person on your team who actually understands how the system behaves in production — not just how it was designed. Youve shipped ML into something real, watched it degrade in ways the offline metrics didnt predict, and built the tooling to catch that next time.

Youre tired of having the interesting architectural decisions made two levels above you. Youd rather own a hard problem end to end, with the authority to decide how it gets solved, than optimise a slice of someone elses.

THE HARD PROBLEMS

Agents that run for months, not minutes. Durable state, replanning, idempotency, recovery. No framework does this.

Credit assignment on sparse, delayed, confounded outcomes — and the offline eval to ship against it, because you cant A/B against real money.

Autonomy with hard guarantees. Actions hit a real ledger. \"Probably right\" isnt available.

Plus the usual, done properly: multi-tenant isolation, high-throughput reconciliation, exactly-once scheduling, flaky third-party APIs, inference cost and latency, observability that still answers questions six weeks later.

ALSO YOURS

Model strategy — what runs where, and the quality/cost/latency tradeoff per decision type.

Deep integration with systems of record — not just reading, but acting in them safely and reversibly, to a standard an auditor accepts.

The product itself — onboarding, dashboards, the control plane that makes a team willing to switch autonomy on.

Plus the founding-engineer tax: CI, observability, on-call, the security questionnaire that unblocks a deal.

WHAT WERE LOOKING FOR

Strong Java and Python, plus real ML engineering experience. Backend discipline alongside the ability to train, evaluate and ship models that survive production.

No fixed years requirement — but well want referenceable experience, not a reading list.

Youve shipped production systems and owned them afterwards.

ML and LLM work beyond prompting: fine-tuning, evals, retrieval, agent orchestration, or the cost/latency tradeoffs that decide whats viable.

Comfortable owning product surface as well as models, and comfortable being the one who decides.

Helpful, not required: fintech, accounting or ERP domain experience; a prior founding-engineer role; financial-grade correctness and audit requirements; anything public that shows you ship.

THE STACK

Java, Python and TypeScript. Anthropic API alongside open-weight models. DigitalOcean, GitHub, Google Workspace. Integrations into NetSuite, Business Central, Sage Intacct and QuickBooks. Slack is a first-class surface, not a notification channel.

Youd have real influence over what this becomes — several of these decisions are still open.

HIRING PROCESS

A simple intro call, then a working session on a real problem. No take-home marathon, no LeetCode.

COMPENSATION

Meaningful founder-level equity, vesting on a standard schedule from your start date.

No cash salary until our seed round closes, at which point the role converts to salary.

HOW TO APPLY

Apply here, or email with something you built that youre proud of and what was hard about it, a repo or demo, and what youd want to own first.

No cover letter. We reply within 72 hours either way.