Machine Learning Engineer

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.