Machine Learning Intern

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 cultureputs 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, the company 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 the company: 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.

Join us as a Machine Learning Intern to help the company's Quantitative Risk team build better ML tools, monitor model performance, and manage credit risk. Over 6 months, you'll help us anticipate business needs and move faster with confidence, working closely with Gauthier Marquand, our Head of Quantitative Risk, and mentored day-to-day by Paul-Adrien Marie, our ML Engineer.

• Formalize modeling guidelines:
  Document and enhance state-of-the-art ML standards so every model the team ships is high-quality and audit-ready.
• Support model risk documentation:
  Help the Model Risk Manager keep model documentation and reporting airtight for regulators.
• Strengthen ML infrastructure:
  Contribute to the CI pipelines and tooling that let the team deploy models reliably.

• Python proficiency:
  You write clean, production-ready code following solid software engineering practices.
• ML foundations:
  You're comfortable with model evaluation, feature selection, and time-series methods.
• Clear technical writing:
  You can turn complex ML concepts into guidelines and reports people actually use.
• AI-native workflow:
  You use AI daily to learn faster and ship better work, and you're always exploring new techniques.
• Fluent English:
  French is a plus, not a requirement.

• Full ML stack exposure:
  See how models go from data to production inside a regulated fintech.
• Cutting-edge techniques:
  Apply recent ML methods and measure their real business impact.
• Production ML skills:
  Build systems that scale, not prototypes that stay in a notebook.
• A tight, senior team:
  Work directly with Gauthier and four other quantitative risk specialists at the intersection of finance and tech.

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

Perks Flex your schedule. Work from home up to 2 days a week with our hybrid policy.

Stay active.

Gympass gives you access to 3,000+ sports and wellness partners.

Eat well.

Lunch card, 50% on us.

Get around. 50% of the cost of your public transportation pass is covered.

Enjoy the rest. Team events, snacks, and beverages.

Take time off. 1 paid day off per month, no questions asked.

Be yourself.

An inclusive, supportive environment where everyone belongs.

At the company, 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.

By applying, you agree that the company processes your personal data to assess your application. Your data is kept for up to 2 years in our candidate pool. Read our Privacy Notice for full details.

On average, our hiring process lasts 15 working days, with 3 steps:

• Interview with our recruiter
• Interview with your future mentor/manager
• Case Study debrief