Senior Machine Learning Engineer
Pubblicato il 29-10-2026 - Pennylane in Roma
Are you looking to have an impact on the daily life of millions of entrepreneurs in France (and tomorrow in Europe)? Are you looking for a work environment that values trust, proactivity, and autonomy? Are our Engineering principles aligned with your vision? Then Pennylane is the right place for you
Si candidi in fretta: consulti la descrizione completa scorrendo verso il basso per scoprire tutti i requisiti di questo ruolo.
Our 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.
About Us
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- 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. Models are commoditizing fast. What makes the difference is our ability to frame the right problem, use Pennylane’s data and accounting knowledge, evaluate quality on real workflows, and operate reliable systems in production. By joining us as a Machine Learning Engineer — Embedded AI, you will turn difficult accounting problems into trusted ML products used every day. You will combine strong ML engineering, product thinking and production ownership to improve both automation and user trust.
HOW you will contribute as a Machine Learning Engineer — Embedded AI
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- You will design and ship ML systems for document understanding, extraction, classification, matching, ranking, scoring and recommendations.
- You will contribute directly to our Copilot and Autopilot experiences, including Bookkeeping Autopilot and Revision Autopilot.
- You will own the full lifecycle of your solutions: problem framing, data and labeling strategy, baselines, training, evaluation, deployment, experimentation, monitoring and maintenance.
- You will turn user corrections and production failures into better datasets, models and product behavior.
- You will define quality metrics that reflect real user value: precision and recall, automation coverage, straight-through processing, human correction rate, latency and cost.
- You will partner with Product, Engineering and accounting experts to understand workflows, define what “correct” means and integrate ML naturally into the user experience.
- You will choose the simplest reliable approach for each problem — deterministic logic, classical ML, deep learning or generative AI — rather than starting from a preferred model.
- You will help improve our shared ML engineering practices: reusable components, experimentation, observability, data quality and reliable training and inference pipelines.
- You will stay ahead of the curve by monitoring emerging ML and AI techniques — including multimodal and generative models — and applying them when they create measurable value.
WHAT You Can Expect From Your Life At Pennylane
Within one month:
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- You will learn about Pennylane, our users, our accounting workflows and our AI vision during onboarding.
- You will get familiar with our ML stack, production systems, datasets, metrics and ways of working.
- You will meet your product and engineering partners and contribute to a first scoped impro