About the role
About The Role
As a Machine Learning Engineer, you’ll do more than build models - you’ll design the systems that make fraud detection possible. You’ll work across modeling, data pipelines, and backend systems (Go) to ensure ML models run reliably, efficiently, and at scale.
This is a chance to combine applied ML with large-scale systems engineering, owning end-to-end solutions that tackle high-stakes, ever-evolving challenges.
What you’ll be doing:
- Build and optimize data pipelines and backend services to process device and behavioral data in real time.
- Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production.
- Turn raw data into production-ready features that feed our fraud detection systems.
- Collaborate with platform and backend engineers to integrate models seamlessly.
- Maintain high standards of security, privacy, and compliance.
- Champion best practices in testing, documentation, and observability.
What you’ll need:
- 5+ years in software engineering, with strong backend experience (Go or Python).
- Hands-on experience with applied ML using large datasets (PyTorch, Scikit-learn, etc.).
- Strong SQL skills and familiarity with relational and non-relational databases.
- Experience with end-to-end ML systems: feature pipelines, model deployment, monitoring, and iteration.
- Excellent communication skills in English, both written and verbal.
- Bachelor's or Master's in Computer Science, Engineering, or a related discipline.
Bonus Points
- Domain knowledge in fraud, risk, or cybersecurity.
- Familiarity with CI/CD, Docker, Kubernetes and the modern devops framework.
- Understanding of modern browser APIs and high-entropy data collection techniques.
