Qonto62mo ago

Staff Machine Learning Engineer for AI Product

France·Paris,ParisRemoteFull-timesenior

Data ScienceOtherMachine Learning EngineerStaff Machine Learning EngineerDataData & AI

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Quick Summary

Overview

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.

Technical Tools

airflowargocdawsfastapikafkapostgresqlprometheuspythonsnowflakemachine-learningmentoring

Responsibilities

~1 min read

→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.

6+ years as an ML Engineer with ML Ops experience: You've developed and deployed client-facing ML products end-to-end — not internal tools or dashboards. You can show measurable impact on real users.
Modelling expertise: Experience building and optimising machine learning models for external customers. You know when to use GenAI and when proven ML techniques are the better choice.
Strong Python engineering: You write resilient, testable code at scale. Proficient with FastAPI (or similar), third-party service integration, and database interaction in production.
ML Ops fluency: Familiar with tools that automate model retraining, performance checking, and drift detection. You've built or significantly improved ML infrastructure before.
Fluent in English: Qonto's working language.

Customer-facing AI with real impact: Your models will be used directly by hundreds of thousands of business customers. You'll see adoption metrics, not just offline evaluations.
A modern, flexible stack: Python, Snowflake, Kafka, Kibana, PostgreSQL, Airflow, AWS, Prometheus, ArgoCD, GitHub, Cursor. You have the freedom to test any tool as long as it helps reach the target.
A team building AI at the core of fintech: 8 AI Engineers and 3 Data Ops working on innovative solutions at the heart of Qonto's financial services — not a side project.
Clear IC growth track: Individual contributor career path for those who want to become deep experts in their field, with access to the latest AI technologies.

Your manager will be Marianne, Head of AI Products at Qonto.

Her path? École Polytechnique graduate. Nearly 6 years at Uber in San Francisco — from Data Scientist to Senior Data Scientist II, leading Strategic Finance Tech and forecasting models for Rides. Then Senior Business Intelligence Engineer at Amazon in Luxembourg. Joined Qonto in 2022 as Head of Data Science, built the team from scratch, and was promoted to Head of AI Products in 2025 — now setting the vision and strategy for AI/ML across Qonto's product.
What does she bring to the team? Deep hands-on ML experience from Uber and Amazon combined with the ability to build and lead teams. She's shipped real-time and batch ML products at scale and cares as much about mentoring and coaching as about model performance. She built Qonto's Data Science team from zero — she knows how to grow people alongside products.

At Qonto, we understand that true diversity isn’t just about ticking boxes on a hiring checklist. Apply regardless of the boxes you tick — who knows? You may have the missing piece of the puzzle we’ve been searching for all along.

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On average, our hiring process lasts 20 working days. More information on our candidate journey here

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