Machine Learning Engineer

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.

Familiarity with leveraging frontier LLMs for automation.