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.

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 - 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.

Join us as a Staff Machine Learning Engineer for our AI Product team to build and ship customer‑facing AI for 500,000+ business customers, combining Generative AI with proven machine‑learning techniques. You’ve delivered client‑facing products end‑to‑end and can show measurable impact (adoption, faster task completion, satisfaction) while ensuring reliability, privacy, and continuous monitoring in production. You must have developed client-facing products. You will work closely with Marianne Ducournau and join a team of 8 AI Engineers and 3 Data Ops, creating innovative solutions that are at the core of Qonto's financial services.

As a Staff Machine Learning Engineer for our AI Product team at Qonto, you will:

• Develop new models end-to-end, from understanding product requirements to implementation and deployment:
• Align with various stakeholders, including Product Managers, Data Engineers, and Backend Engineers to ensure seamless integration of ML solutions into the product ecosystem
• Develop models: design, train, evaluate, and iterate on ML models using modern techniques tailored to real business problems
• Put models into production with robust technical implementation and quality assurance processes
• Scale our solutions:
• Create an ML Ops framework for the team to ensure our models scale effectively with proper monitoring and alerts (e.g., model drift detection, performance tracking, automated retraining pipelines)
• Share best practices within the ML team, contributing to internal knowledge, tooling improvements, and mentoring peers

What you can expect:

• Market/Team Context: Your work will have visible and direct impact on Qonto's users and experience.
• Methodologies and tools: We use a modern tech stack including Python, Cursor, Snowflake, Kafka, Kibana, PostgreSQL, Airflow, AWS tools, Prometheus, ArgoCD, and GitHub. You'll have the freedom to test any tool as long as it helps reach the target.
• Growth opportunities: There's a clear individual contributor track for those who want to become experts in their field and the opportunity to work on the latest AI

Your Future Manager Your Future Manager will be Marianne, Head of Data Products.

Her background? After her experience in famous tech organizations where she managed Data Science teams in the Finance department, Marianne joined Qonto 3 years ago to build our Data Science team!

What does she bring to the team? As an expert in her field, she has hands-on experience in implementing Data Science models to serve cross-functional teams and deliver actionable insights. Marianne also has a true passion for mentoring and coaching the team.

About You:

• Experience: You have 6+ years of experience ML Engineer coupled with ML Ops, particularly in developing client-facing products. You're familiar with tools that automate model retraining and performance checking.
• Modeling expertise: You have experience building and optimizing machine learning models for external clients.
• Software Engineering: You're proficient at writing resilient, high-quality, testable code in Python, and you understand how to integrate with third-party services and databases at scale and FastAPI or a similar web framework.
• Problem-solving: You have a proven track record of identifying complex problems and implementing effective solutions in machine learning contexts.
• Proactivity: You take the initiative to improve processes and don't wait for problems to arise before addressing them.
• Language: You are fluent in English.