We are excited to invite applications for the position of ML Engineer to join our growing team of Data Scientists (DSs) and Machine Learning Engineers (MLEs). Our team is a central entity that develops and deploys machine learning capabilities across Mollie, contributing to various Domains, including Monitoring (Risk & Fraud), Payments, Merchant Experience, Financial Services, Go-to-Market, and more. We have built and maintain a multi-cloud ML Platform for model development and production, as well as MollieGPT, our in-house GenAI solution for both internal employees and our customers.
This role is hands-on, where you will spend most of your time coding in Python and Terraform alongside other ML Engineers and Data Scientists. The position is based at Mollie's Lisbon Hub, and you will be part of a geographically distributed (Amsterdam/Lisbon/Milan) team that embraces remote and hybrid collaboration.
What you'll be doing
Collaborate closely with ML Engineers, Data Scientists, and various engineers across Mollie.
Contribute to the design, development, and maintenance of our scalable, low-latency cloud ML Platform and services.
Deploy ML models to production in partnership with Data Scientists.
Promote and implement best practices and standards in MLOps.
Document MLOps workflows and architecture, ensuring compatibility with other systems at Mollie, including compliance with security standards (e.g., threat modeling).
What you'll bring
1-3 years of proven experience as an ML Engineer (or similar role), including developing and maintaining ML Platforms and Model Pipelines in production environments.
Advanced software engineering skills with a passion for coding in Python.
Familiarity with at least one major cloud ML platform, preferably Google Cloud's Vertex AI.
Experience with containers and container orchestration, such as Docker, Kubernetes, and Kubeflow.
Proficiency with Terraform or similar infrastructure-as-code (IaC) tools.
Comfort with using a Linux shell and Git for version control.
Knowledge of the Model Development Lifecycle and common DS libraries, such as scikit-learn, pandas, shap, feature-engine, xgboost (or lgbm), and MLflow.
Attention to detail with the ability to quickly shift priorities when required.
Strong presentation skills and the ability to communicate effectively with diverse audiences.
Enjoy working collaboratively in a cross-functional and distributed team environment.
Comfort with Agile methodologies, such as Scrum, Kanban, or similar frameworks.
Nice to have:
Experience with LLMOps, i.e. deploying and managing GenAI solutions in production.
Experience with (Py)Spark and managing Spark clusters.
Experience in the financial services industry (banking or fintech).
M.Sc. in Machine Learning, Computer Science, or Computer Engineering.
Benefits:
MacBook
Birthday off
Complimentary baby days
20 days working from abroad
22 holiday days
Work from home budget
Bike lease plan
Pension contribution
Health insurance
Equity plans
Referral bonus
Learning platform
How we hire Apply: Our Talent Acquisition team and hiring manager will review your application and respond within 2 weeks.
Screening call: If you seem like a Mollie-in-the-making, we'll invite you to a screening call so we can learn more about each other.
Are you the one?: You'll have two or more interviews. And if it's a highly technical role, we'll also assess the specific skills you'll need.
Diversity, Equity & Inclusion At Mollie we are as diverse as we are united. That means we bring open hearts and minds to work, and nurture a culture that feels like home. We celebrate diversity of people and perspectives and are proud to be an equal opportunity employer.
Every new Mollie is hired on the basis of qualifications, merit, and business need. We do not discriminate. We value our differences because we know that our individual perspectives make our products and culture stronger. So we encourage everyone to be their authentic selves and we prioritise respect.
At the end of the day, we are a team of individuals – diverse yet united by our vision to eliminate financial bureaucracy.
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