Two seats that decide what this becomes

The role

Full-stack, leaning hard into ML. You'd 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

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

You're tired of having the interesting architectural decisions made two levels above you.

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 can't A/B against real money.
• Autonomy with hard guarantees. Actions hit a real ledger. "Probably right" isn't 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 we're 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 we'll want referenceable experience, not a reading list.

• You've 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 what's 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.

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

Compensation

Hiring process

Two steps. No take-home marathon, no LeetCode.

• Intro call with the founder. What you've built, what you want to own, how the comp structure lands for you.
• Solve a real problem — one session, from our actual domain. Use whatever you like, including Claude Code, Cursor or any other agent. We care how you think and how you use the tools.

The role

The first GTM seat, and it's yours to define. We have a product live with paying customers and a founder who has been closing the deals. What we don't have is the person who turns founder-led selling into a repeatable revenue machine.

Why this is worth your time

• You're early enough for it to matter. Not the fourth AE inheriting a territory and someone else's playbook. You'll write the playbook — ICP, messaging, objection handling, the first pipeline.
• The category is moving now. AR automation is a ~$4.8B market compounding at ~13% a year, and 87% of CFOs say AI is a priority (Deloitte CFO Signals). Every mid-market controller you call has a collections problem and a board asking what they're doing about AI.
• You'd be selling something real. Agents live in production against real ledgers — early, but working, not a design-partner waitlist. Our wedge is inbound payer handling: auto-answering "why is this invoice wrong?" and classifying why cash is stuck. It's where the incumbents are weakest and where demos land.
• Direct line to the founder. No sales ops layer, no committee.

What you'll own

• Outbound to mid-market CFO, controller and AR leadership
• The full cycle: discovery, demo, security and procurement, close
• Turning founder-led deals into a documented, teachable motion
• Pricing and packaging input, with real market feedback
• The GTM stack — CRM, sequencing, enrichment

What we're looking for

• You've closed B2B SaaS into finance, accounting or ERP buyers — ideally mid-market
• A track record you can point to: quota attainment, logos, deal sizes
• You've been the only seller before, and sourced your own pipeline
• Fluent enough in AR, cash application, ERP workflows or fintech to hold a real conversation with a controller
• High tolerance for ambiguity and a bias toward writing things down

Helpful, not required: early-stage startup experience pre-Series A; relationships in the mid-market CFO ecosystem; familiarity with NetSuite, Business Central, Sage Intacct or QuickBooks.

Compensation