Senior Machine Learning Engineer - Embedded AI

Role overview

A senior machine learning role within the Embedded AI team of a fast-growing European fintech building an accounting and financial operations platform for small businesses and accounting firms. The team owns the specialised ML systems behind in-product AI assistants, including invoice parsing, document classification, accounting suggestions, matching and automated bookkeeping flows, turning raw accounting data into trusted, production-grade ML products.

Responsibilities

- Design and ship ML systems for document understanding, extraction, classification, matching, ranking, scoring and recommendations. - Contribute directly to Copilot and Autopilot experiences, including Bookkeeping and Revision Autopilot flows. - Own the full lifecycle of solutions: problem framing, data and labelling strategy, baselines, training, evaluation, deployment, experimentation, monitoring and maintenance. - Convert user corrections and production failures into better datasets, models and product behaviour. - Define quality metrics that reflect real user value, such as precision and recall, automation coverage, straight-through processing, human correction rate, latency and cost. - Partner with product, engineering and accounting experts to integrate ML naturally into the user experience, choosing the simplest reliable approach for each problem.

Requirements

- Strong ML engineering skills combined with product thinking and a sense of production ownership. - Experience designing problem framings, evaluation strategies and reliable production ML systems end to end. - Ability to work with cross-functional teams, including subject-matter experts, on workflow-heavy, high-trust domains. - Comfort balancing deterministic logic, classical ML, deep learning and generative AI depending on the problem.

Nice to have

- Experience with accounting, fintech or other high-trust business workflows. - Familiarity with human-in-the-loop systems, active learning or learning from user corrections. - Experience running ML at scale under strict latency, reliability or cost constraints. - Exposure to multimodal or generative models for document understanding.

Benefits and work setup

- Remote-friendly culture with international teammates and significant distributed-work flexibility across Europe.