Maximor AI — Senior AI 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: Co-founders

Maximor is building the AI operating system for the CFO office — Audit-Ready AI Agents that connect to a company's existing finance stack and automate the full order-to-cash, record-to-report, treasury, and financial-reporting workflow, so finance teams review exceptions while AI handles the rest. Raised $9M led by Foundation Capital, alongside Aravind Srinivas (CEO of Perplexity) and finance leaders from Ramp, Gusto, and the Big Four.

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

• Own a domain end-to-end: No PM writes your specs and no architecture committee gates your ideas — you own a finance domain across context, prompts, tools, evals, guardrails, and UX.
• Hard, novel problems: Building agents finance teams and auditors can trust, context engineering over large messy financial data, and durable orchestration across flaky enterprise systems.
• Strong backing and pedigree: $9M led by Foundation Capital, with the CEO of Perplexity and finance leaders from Ramp, Gusto, and the Big Four behind the company.

• Not provided on the role page.

Own a finance domain end-to-end — from context engineering and prompt design through tool use, evals, guardrails, and the UX around them — working directly with controllers, accountants, and CFOs to replace manual processes with production-grade agent systems. Backend-heavy, full-stack in practice.

• →Own a finance domain end-to-end, building the agent system that automates it across context, prompts, tools, evals, guardrails, and UX, working directly with controllers and CFOs
• →Design and implement durable, replay-safe orchestration for long-running AI workflows across flaky, stateful enterprise systems
• →Build the trust layer for non-deterministic systems: evals, verification, guardrails, and observability that catch agent mistakes before a human does
• →Ship full-stack when the work calls for it, owning decisions across the LLM pipeline, infrastructure, backend, and UX within your pod
• →Do serious context engineering: retrieval, memory, and tool design that gets agents to reason reliably over large, messy, proprietary financial data
• →Build idempotent, audit-ready write-back systems for ERPs and financial systems with full traceability

Tech stack: Python (primary stack)

• 2+ years building and shipping production AI agents
• 4+ years backend or infrastructure engineering
• Research background in ML, NLP, or AI agents
• Early-stage startup experience (pre-seed to Series B)
• NYC in-person, startup hours, 6 days a week, 9am to 7pm or 8pm

• Strong agentic systems track record
• Research background in ML, NLP, or agents
• Graduated post-2021 with direct agentic-era experience
• Fintech, ERP, or accounting software background

• Frontend-heavy background with little backend depth
• Generic AI resume without architectural depth
• Multiple short tenures

• Salary — $170,000–$220,000
• Equity — 0.1%–0.35%
• On-site policy — In-person, New York City office · 6 days/week, 9am–7/8pm
• 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 Stage 3 — Take-home Assignment (5–8 hours) Stage 4 — Take-home Debrief Stage 5 — On-site (5–6 hours) Stage 6 — Offer Extended Stage 7 — Hired — Candidate accepts and starts.