Senior Machine Learning Engineer - Embedded AI

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

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

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.

• 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.

• 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.

• 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.

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.

• 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)