AI accountant

autoMEE builds FlowMEE - an AI accounting platform designed to handle repetitive accounting work end-to-end while keeping finance teams in control (approval flows, audit logs, overrideable decisions, and clean integrations).

We're now launching in the UK. This role is for someone who speaks "accounting" and "automation" fluently - and wants to help ship a product that actually works in real finance teams, not just in demos.

Location: Remote

Type: Full-time

Start: ASAP

You'll be one of the core people shaping FlowMEE's UK readiness and rollout: mapping UK accounting reality into product rules, validating AI outputs, building the implementation playbook, and deploying with early customers.

This is not a traditional accountant role. It's closer to: Accountant + Product + Implementation + QA for AI.

If you enjoy turning messy accounting edge cases into clean rules, building systems, and shipping practical automation - you'll feel at home.

Accounting & UK domain

• Strong hands-on experience in UK accounting operations (industry, practice, or shared services)
• Solid understanding of UK VAT, common filings, and month-end routines
• Comfortable with management accounts logic and reconciliations (bank, AP/AR, balance sheet)

Systems & AI mindset

• Experience using cloud accounting software (Xero / QuickBooks / Sage) and handling real-world data quirks
• You think in workflows: inputs validations approvals outputs
• You can explain accounting decisions clearly (written + spoken English is a must)
• You're detail-obsessed and pragmatic: accuracy matters, but shipping matters too

Nice-to-haves

• Experience implementing accounting systems or automation (Zapier/Make, RPA, scripts, APIs)
• Experience in accounting tech, fintech, or AI-assisted finance tooling
• Exposure to audit/compliance practices (controls, evidence trails, process documentation)

• Translate UK accounting workflows into FlowMEE configurations (posting logic, exceptions, approval routes)
• Create and maintain a UK rule library (VAT treatments, typical spend categories, edge cases)
• Define what "good" looks like: acceptance criteria for postings, reconciliations, and outputs

• Validate AI-generated postings and document handling (accuracy, explainability, audit trail)
• Build QA test sets (realistic scenarios + nasty corner cases)
• Track errors, root causes, and drive fixes with the product/engineering team

• Lead implementation for early UK customers: discovery setup training pilot rollout
• Build repeatable deployment playbooks and documentation
• Help define onboarding timelines, customer responsibilities, and ROI measurement