Machine Learning Engineer (Fraud Detection) - REMOTE / FULLTIME

Machine Learning Engineer

Full-time

Remote

Seniority

5 - 8 years of experience in software engineering with strong backend and machine learning work

Work experience

End-to-end ML model ownership: feature pipelines, model deployment, monitoring, and iteration (not just experimentation)

Depth in backend software engineering over data science

Fraud domain experience (bot detection, device fingerprinting, VPN/proxy detection, etc.)

Education

BS or MS in Computer Science, Engineering, or related field

Hard skills

Built latency-sensitive ML systems serving real-time predictions at scale

Familiarity with ML platform tooling: feature pipelines, drift monitoring, model iteration cycles

Experience with Go for backend services (or demonstrated ability to pick up new languages quickly)

Soft skills

Self-directed: navigates ambiguity and delivers with minimal hand-holding after onboarding

Traits to avoid

Not looking for just ML ops

Pure model-building focus with no production deployment or infrastructure experience

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.