Senior Software Engineering Manage

About Hyphenate

Most AI companies are building copilots.

Hyphenate is building the AI operating system for the CFO office. Our Audit-Ready AI Agents connect to a company's existing finance stack-ERPs, banks, billing, payroll, CRM, contracts, spreadsheets, email, and Slack-and automate the work behind behind the entire order to cash process, record to report process, treasury management, financial reporting and and audit readiness.

The goal isn't to help the finance team write better prompts.

The goal is for finance teams to review exceptions while AI does the rest.

What makes Hyphenate different is our Unified Finance Context -a financial understanding layer that captures transactions, policies, contracts, historical decisions, and accounting judgment. On top of it sit Audit-Ready AI Agents that can reason, explain their decisions, escalate uncertainty, and continuously improve.

We've raised $9M led by Foundation Capital , alongside BoldCap, Gaia Ventures, Aravind Srinivas (CEO of Perplexity) , and finance leaders from Zuora, Ramp, Gusto, MongoDB, Zoom, and the Big Four.

What You'll Own

You won't own tickets-you'll own a piece of the future accounting stack.

Every senior engineer owns a finance domain (revenue, cash, close, reporting, payroll, fixed assets, tax, or controls) and ships it end-to-end with a pod of 2-3 engineers.

No PM writes your specs. No architecture committee approves your ideas. You work directly with controllers, accountants, and CFOs to understand the problem, then build the system that replaces the manual process.

Problems Worth Your Brain

How do you build AI agents that finance teams and auditors can trust? Verification, guardrails, observability, and evaluation systems for non-deterministic AI.

How do you give an agent the right financial context? Context engineering over transactions, ledgers, contracts, policies, and historical decisions-without bloat, drift, or data leakage.

How do you turn messy enterprise systems into a unified source of truth? Ingest, normalize, and reconcile data from ERPs, banks, payroll, billing platforms, CRMs, and email.

How do you orchestrate durable AI workflows in the real world? Long-running, replay-safe workflows across flaky, stateful enterprise systems.

How do you safely write back to systems of record? Idempotent, audit-ready updates to ERPs and financial systems with full traceability.

A Few Strong Opinions

• The engineer who can't operate AI agents fluently is becoming obsolete, fast. Fluency with Claude, Cursor, and internal agent tooling is a second cortex.
• "Backend vs. frontend" is dissolving. The agents do the typing. The constraint is product judgment, system design, and the ability to close the loop from problem to shipped feature. Our engineers ship full-stack when the work calls for it.
• The pod is the unit of leverage. Two engineers who can hold an entire module in their heads ship more than ten engineers who each own a slice. Specialization across pods, generalist within them. Our engineers raise the ceiling for every pod.
• The accountant + engineer loop is the moat. Engineers who sit with controllers and build from what they see compound faster than the ones who don't.

What Great Looks Like Here

You can learn a finance workflow in the morning and ship it by the end of the day. The ability to quickly understand a domain and translate it into software is the single most important skill on the team.

You think like an owner. You're a current or future founder who scopes your own work, thinks from the customer's perspective, owns decisions, and drives outcomes without waiting for direction.

You solve problems end to end. The team is split vertically, so every engineer owns a part of the product and makes decisions across the LLM pipeline, infrastructure, backend, and UX.

You care about getting it right. A 100% solution beats an 80% one. When something breaks, you go to root cause.

You operate AI coding agents at a high level. You have opinions about which agent to use for which task. You've shipped real production code through them.

What You Should Have Done Before

• 5+ years of software engineering , mostly backend or infrastructure.
• Strong backend chops in a modern language - Python, Go, Java, Rust, or similar. Our stack is Python; if your deep experience is elsewhere, ramp fast.
• Built hard backend systems - distributed pipelines, transactional or ledger systems, integration platforms, or workflow engines.
• Worked at an early-stage startup (pre-seed through Series B).

You'll Stand Out if

You've worked in fintech, ERP, accounting, payments, banking, treasury, audit, or compliance software.

You've built production AI agents, not just demos.

You've built evaluation frameworks, verification systems, or observability platforms for AI.

You've worked with data warehouses, financial systems, or enterprise integrations.

You've been the engineer customers asked for by name.

The Upside

Exceptional teammates with high ownership and direct access to customers, CFOs, controllers, and founders.

Top-of-market compensation and meaningful early-stage equity.

Full medical, dental, and vision coverage for employees and dependents, plus 401(k) match.

Meals, a stocked NYC office, and the chance to help define an entirely new category: Audit-Ready AI for Finance.