Machine learning engineer for accounting workflows

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.

• 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.

• 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.
• Будет плюсом: 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

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