Maximor AI — Senior Software Engineer

Type: Full-time | On-site | New York City, NY Compensation: $170,000–$220,000 + 0.1%–0.35% equity Hiring count: 1 Visa sponsorship: None available Reports to: Founding engineering team

Maximor is building the AI operating system for the CFO office, connecting directly to a company's existing finance stack to automate the full order-to-cash cycle, record-to-report process, treasury management, and financial reporting. Its Unified Finance Context layer captures transactions, policies, contracts, and accounting judgment, powering Audit-Ready AI Agents that can reason, explain their decisions, and escalate uncertainty.

Founded: 2023 | Team size: 1–10 | Total funding: $9M Industry: AI Tools Website: maximor.ai Office: New York City, NY

Backing (from outreach template + role body): $9M led by Foundation Capital, alongside Aravind Srinivas (CEO of Perplexity) and finance leaders from Ramp, Gusto, and Zuora.

• Full domain ownership: Each senior engineer owns a finance domain end-to-end — no PM writing specs, no architecture committee to approve decisions.
• Frontier backend problems: Building AI agents finance teams and auditors can trust, turning messy enterprise integrations into a unified source of truth, and orchestrating durable, replay-safe workflows across flaky stateful systems.
• Strong backing: $9M led by Foundation Capital, with the CEO of Perplexity and finance leaders from Ramp, Gusto, and Zuora behind the company.

• Not provided on the role page.

A high-ownership engineering seat where each senior engineer takes full responsibility for a finance domain — from the LLM pipeline and infrastructure through to the customer-facing surface — working directly with controllers, accountants, and CFOs to understand the problem before building the system.

• →Own a finance domain end-to-end (revenue, cash, close, reporting, payroll, fixed assets, tax, or controls) and ship it with a pod of two to three engineers
• →Design and build AI agents that reason across fragmented financial data, with verification, guardrails, observability, and evaluation systems for non-deterministic outputs
• →Ingest, normalize, and reconcile data from ERPs, banks, payroll platforms, billing systems, CRMs, and email into a unified financial context layer
• →Ship idempotent, audit-ready write-backs to systems of record with full traceability
• →Sit directly with controllers and finance teams to learn workflows, then translate that understanding into shipped software
• →Operate AI coding agents fluently as a daily part of the engineering workflow, owning decisions across the LLM pipeline, infrastructure, backend, and product surface

Tech stack: Python (backend engineering also in modern languages such as Go, Java, or Rust)

• 5+ years backend or infrastructure engineering
• Seed-to-Series B startup OR high-growth company (Rubrik, Databricks, Ramp, Brex) OR top large-tech (Meta, Google, Stripe, LinkedIn)
• Hard backend systems: distributed pipelines, ledger/transactional systems, integration platforms, or workflow engines
• Resume showing architecture detail and clear personal ownership
• NYC in-person
• Fintech, ERP, or accounting-software background a strong plus
• Startup hours, 6 days a week, 9am to 7pm or 8pm

• Startup or high-growth pedigree with real ownership
• Hard backend systems builder
• Domain depth in finance, fintech, or enterprise integrations
• Production AI agent experience

• Frontend-heavy background with shallow backend depth
• Resume lists technologies without depth or ownership evidence
• Multiple short stints (more than one role under one year)

• Salary — $170,000–$220,000
• Equity — 0.1%–0.35%
• On-site policy — In-person at the NYC office; startup hours, 6 days a week, 9am to 7pm or 8pm
• Visa sponsorship — None available
• Employment type — Full-time
• Location — New York City, NY

• Not provided on the role page.

Stage 1 — Pending Approval — Candidates awaiting initial approval. Stage 2 — Founding engineer screen — Initial technical and cultural-fit conversation with a founding engineer. Stage 3 — Take-home assignment (5–8 hours) — Demonstrates backend engineering depth and ownership approach. Stage 4 — Debrief on take-home — Walkthrough and discussion of take-home work. Stage 5 — On-site (5–6 hours) — Deep technical and team interviews with engineers and founders. Stage 6 — Offer Extended Stage 7 — Candidate Hired — Candidate accepts and starts.

Not provided as a distinct section on the role page. The Requirements name the following as example pedigree companies: High-growth — Rubrik, Databricks, Ramp, Brex Top large-tech — Meta, Google, Stripe, LinkedIn