Senior Machine Learning Engineer
Pubblicato il 01-10-2026 - Pennylane in Roma
Are our Engineering principles aligned with your vision? We aim to become the most beloved financial Operating System of French SMEs and Accounting Firms (and soon, European ones). We help entrepreneurs rid themselves of time-consuming tasks related to accounting and finance while giving them access to the key financial information they need to make the best decisions for their business.
Make ourselves known as a groundbreaking accounting and financial software for small businesses and their accountants Raise a total of €400 million, including from Sequoia — the famous Silicon Valley fund that invested early in companies like Google, Facebook, Airbnb, Stripe and Paypal Grow from 7 cofounders to 1,000 happy Pennylaners, and earn a place among the greatest companies to work for in France (and remotely), with a 4.6/5 rating on Glassdoor Build an international environment with more than 25 nationalities and a strong remote-friendly culture, where 30% of employees already work from all parts of Europe Earn the trust of thousands of customers and accounting firms,
with outstanding ratings Reach more than 1,000,000 small and medium‐sized enterprises (SMEs) and over 6,000 accounting firms using Pennylane in France WHY this position is of utmost importance to reach our mission Pennylane is becoming the financial OS of European SMEs and accounting firms. A growing share of the work done inside our product will be automated or assisted by AI. Our strategy operates at three layers: verticalized AI embedded in the product, an agent interface that makes Pennylane readable and actionable by AIs, and the Pennylane orchestrator agent itself.
The Embedded AI team owns the first layer. We build the specialized machine learning systems behind Copilot and Autopilot: invoice parsing, document classification, accounting suggestions, matching, scoring, Bookkeeping Autopilot and Revision Autopilot. What makes the difference is our ability to frame the right problem, use Pennylane's data and