Staff Machine Learning Engineer for AI Product

Our mission and customers: We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools.

We are proud to be rated 4.8 on Trustpilot, based on 55,000+ reviews.

Our culture puts customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75 (more about our culture here ).

Our journey: Founded in 2017 by Alexandre and Steve, Qonto has grown to 1,600+ Qontoers serving over 600,000+ customers across 8 European countries.

We have been profitable since 2023, and we are just getting started.

Our beliefs: We hire for skills and potential.

With 80+ nationalities, 45% women, of which 56% of women in our leadership team, diversity isn't a program; It's who we are.

We've built a discrimination-free hiring process because the best teams are built on merit.

AI at Qonto: AI is deeply embedded in how we work ( here ) - Every Qontoer gets unlimited access to the best AI tools.

We want people who experiment without waiting for permission, push AI beyond the obvious, know when to trust it, and when to question it.

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Join us as a Staff Machine Learning Engineer on our AI Product team to build and ship customer-facing AI for 600,000+ business customers.

You'll combine Generative AI with proven machine-learning techniques to create products with measurable impact — adoption, faster task completion, user satisfaction — while ensuring reliability, privacy, and continuous monitoring in production.

➡️ What you'll do

• Develop ML models end-to-end: From understanding product requirements to training, evaluating, and deploying models in production. You design, iterate, and ship — not just prototype.
• Integrate ML into the product ecosystem: Align with Product Managers, Data Engineers, and Backend Engineers to ensure your models are seamlessly embedded in Qonto's financial services.
• Build the ML Ops framework: Create the infrastructure for the team to scale — model drift detection, performance tracking, automated retraining pipelines, monitoring, and alerts.
• Put models into production with rigour: Robust technical implementation, quality assurance, and continuous monitoring. Client-facing AI in financial services has no room for silent failures.
• Raise the bar for the team: Share best practices, contribute to internal tooling improvements, and mentor peers across the ML team.