Senior Machine Learning Engineer (Fintech)

Location: Hybrid in Warsaw, Poland

Company

hirify.global provides regulated, wallet-based payment infrastructure for complex multi-party fund flows, enabling platforms to collect, secure, split, hold, and pay out funds.

What you will do

• Build and improve fraud detection solutions for fraudulent payments, identity theft, and account takeover.
• Investigate transactional and behavioural data, identify fraud patterns, and convert findings into actionable insights.
• Research algorithms and data sources; develop, evaluate, deploy, and maintain machine learning models processing millions of transactions.
• Build and maintain data pipelines and datasets for feature engineering, model training, and real-time inference.
• Develop and operate reliable, scalable, performant, and secure production ML services, internal tools, and workflow automations.
• Lead end-to-end data science projects from opportunity identification and solution design to measurable product and client impact.

Requirements

• At least 5 years of professional experience in Data Science, applied Machine Learning, or a closely related field.
• Strong knowledge of machine learning algorithms, statistics, feature engineering, model evaluation, and validation techniques.
• Excellent Python skills, including pandas, NumPy, and scikit-learn; strong SQL skills with large, complex datasets.
• Good software engineering practices covering clean code, testing, version control, and maintainability.
• Solid understanding of software architecture and design principles, with the ability to build maintainable and extensible solutions.
• Practical experience in either Machine Learning Engineering, including production deployment, monitoring, maintenance, lifecycle management, and deployment practices, or Data Engineering, including pipelines, transformations, and reliable datasets using modern data platforms.
• Willingness and ability to develop expertise in the less familiar of Machine Learning Engineering and Data Engineering.
• Strong analytical and problem-solving skills, independent handling of complex technical challenges, pragmatic business mindset, and excellent communication skills.
• Very good English and Polish language skills.

Nice to have

• AWS, particularly Amazon SageMaker.
• Kubernetes, Docker, and CI/CD for production service deployment and operation.
• Apache Kafka or other event streaming technologies.
• Terraform or other Infrastructure as Code tools; Snowflake, dbt, or similar data transformation and warehouse technologies.
• Workflow automation with tools such as n8n.
• AI agent development or generative AI applied to business problems.
• A degree in mathematics, computer science, quantitative methods, econometrics, or engineering.
• Experience in fintech, banking risk, credit, or fraud detection.

Culture & Benefits

• Full-time hybrid work in Warsaw, Poland.
• Ownership of solutions from initial exploration through production and the opportunity to shape data and ML engineering practices.
• Focus on technical curiosity, initiative, pragmatic problem-solving, collaboration, and continuous learning.

Hiring process

• HR call.
• Take-home test.
• Case study interview with a presentation to the Hiring Manager and VP, followed by an offer.