Maximor builds AI agent–powered finance software that automates reconciliation and month-end close tasks traditionally done in Excel.
ABOUT MAXIMOR Most AI companies are building copilots. We're building AI accountants that work. Maximor 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 the close, revenue recognition, reporting, cash management, and audit readiness. The goal isn't to help accountants write better prompts. The goal is for finance teams to review exceptions while AI does the rest. What makes Maximor 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 a module—you'll own the systems every module depends on. Staff engineers build the platform layer that powers every pod: agent frameworks, context systems, orchestration, verification, observability, data infrastructure, and developer tooling. Your work multiplies the output of the entire engineering team. No PM writes your specs. No architecture committee approves your ideas. You identify the bottlenecks limiting the company, work directly with engineers, accountants, and customers, and build the abstractions that make hard problems simple. Staff at Maximor is depth of impact, not span of control. We don't stop writing code. PROBLEMS WORTH YOUR BRAIN - Own the agent harness the entire company builds on. Define the abstractions for context, verification, guardrails, observability, and developer tooling so every pod can ship audit-grade AI agents on shared rails. - Design the financial context graph. Build the structured context layer every agent reasons over—ledger state, dimensions, policies, contracts, precedent, and entitlements—while keeping it coherent, scalable, and multi-tenant safe. - Make trust a property of the platform. Define the verification, auditability, evals, and observability standards that determine whether AI output is safe enough for a customer's books—and enforce them by construction. - Architect the financial data platform. Own the ingestion, normalization, reconciliation, and canonical ledger model that turns data from ERPs, banks, billing, payroll, CRMs, and email into a trustworthy source of truth. - Own the orchestration and write-back backbone. Design the durable execution layer for long-running AI workflows and the exactly-once, audit-ready path that safely writes back to ERPs and systems of record. 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. Staff 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. Staff 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 identify the platform abstraction by the afternoon. You quickly understand domains, spot patterns across products, and turn one-off solutions into reusable systems. - You think like an owner. You scope ambiguous problems, obsess over customer outcomes, make decisions with conviction, and drive results without waiting for direction. - You create leverage for the entire engineering team. You build abstractions, frameworks, and architectural patterns that make every pod faster and more effective. - You solve the hardest problems end to end. From AI systems and infrastructure to data platforms and product architecture, you operate comfortably across the stack. - You care about getting it right. You chase root causes, raise engineering standards, and make quality, reliability, and correctness scale with the company. - You operate AI coding agents at a high level. You have strong opinions about how humans and agents should collaborate and have shipped significant production systems using them. WHAT YOU SHOULD HAVE DONE BEFORE - 8+ years of software engineering experience, with deep expertise in distributed systems, data infrastructure, workflow engines, transactional systems, integration platforms, or AI/agent platforms. - 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. Ships TypeScript when needed. - Built platform systems that multiplied the output of other engineers. You've designed abstractions, frameworks, or infrastructure that multiple teams depended on and that continued to compound over time. - Led architectural decisions under ambiguity. You've identified foundational problems, made technical bets with incomplete information, and seen the long-term consequences of those decisions. - Thrived in an early-stage startup environment (pre-seed through Series B), where ownership is high, resources are constrained, and there is no separation between strategy and execution. YOU’LL STAND OUT IF - You've been a Staff or Principal IC at a high-growth startup and have built systems that multiple teams depended on. - You've worked in accounting