Senior Machine Learning Engineer - Embedded AI
About this role
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!
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
Pennylane is one of the fastest growing Fintechs in France (and soon in Europe!). In 5 years of existence, we've managed to:
💻 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
You will join the Embedded AI team within our ML & AI organization. The team works closely with product squads and accounting experts, from initial exploration to production, monitoring and continuous improvement.
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:
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 improvement.
Within 3 months:
You will own an Embedded AI use case end to end, with clear offline and production metrics.
You will have shipped a meaningful improvement to a Copilot or Autopilot capability.
You will be comfortable with our technical stack, including Python, Pytorch, PySpark, Redshift, Airflow, AWS SageMaker and our monitoring tools.
You will use real user feedback and error analysis to prioritize the next iterations.
Within 6 months:
You will lead larger cross-team ML projects and help shape the Embedded AI roadmap.
You will improve the reliability, automation coverage and maintainability of one or more production systems.
You will share best practices and raise the bar for ML engineering, evaluation and production ownership across the team.
And beyond: the ML & AI teams will continue growing with the company
This means opportunities to mentor new team members, lead major product initiatives and help define the technical standards that make trusted AI possible at Pennylane.
Who are we looking for?
You may be a great fit if you:
Have 5–8 years of experience and are very strong in Python.
Have hands-on experience building and operating machine learning systems at scale in production — not only training models or running notebooks.
Know how to frame an ambiguous product problem, establish a baseline and select useful metrics before optimizing a model.
Have strong experience in several of these areas: document AI, NLP, classification, ranking, recommendation, anomaly detection, deep learning or generative AI.
Care about data quality, observability, failure modes, cost and long-term maintainability as much as model performance.
Have a balanced blend of technical, business and product skills, and communicate well with software engineers, product managers and non-technical domain experts.
Are fluent in English; French is not mandatory.
Nice to have:
Experience with accounting, fintech or other high-trust business workflows.
Experience with human-in-the-loop systems, active learning or learning from user corrections.
Experience running ML systems at scale with strict latency, reliability or cost constraints.
Familiarity with multimodal or generative models for document understanding.
What does the recruitment process look like?
A first interview with our Talent Acquisition Manager
A case study interview covering problem framing, data, modeling, evaluation, deployment and monitoring (75 min)
A past project interview to discuss your experience and technical decisions (60 min)
An interview with our Tech & Product leaders to discuss our company culture (30 min)
🇫🇷 Données personnelles
Pennylane traite vos données pour gérer votre candidature et évaluer votre adéquation au poste. Si votre candidature n’aboutit pas, vos données peuvent être conservées 2 ans à compter de notre dernier échange ou de la clôture du recrutement afin de constituer & gérer un vivier de candidats. Vous pouvez vous opposer à tout moment et demander la suppression de vos données en écrivant à :
Notre data policy
🇬🇧 Personal Data
Pennylane processes your data to manage your application and assess your suitability for the position. If your application is unsuccessful, your data may be retained for 2 years from our last exchange or the closing of the recruitment process, in order to build and manage a candidate pool. You may object at any time and request the deletion of your data by writing to Learn more
🇩🇪 Personenbezogene Daten
Pennylane verarbeitet Ihre Daten, um Ihre Bewerbung zu bearbeiten und Ihre Eignung für die Stelle zu beurteilen. Sollte Ihre Bewerbung nicht erfolgreich sein, können Ihre Daten bis zu 2 Jahre ab unserem letzten Austausch oder dem Abschluss des Rekrutierungsverfahrens gespeichert werden, um einen Kandidatenpool aufzubauen und zu verwalten. Sie können jederzeit Widerspruch einlegen und die Löschung Ihrer Daten beantragen, indem Sie an Mehr erfahren
🇪🇸 Datos personales
Pennylane trata sus datos para gestionar su candidatura y evaluar su idoneidad para el puesto. En caso de que su candidatura no sea seleccionada, sus datos podrán conservarse hasta 2 años a partir de nuestro último contacto o de la finalización del proceso de selección, con el fin de constituir y gestionar una reserva de candidatos. Puede oponerse en cualquier momento y solicitar la eliminación de sus datos escribiendo a Más información
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