Machine Learning Engineer

Role overview

You’ll be a Machine Learning Engineer designing end-to-end systems that enable fraud detection. The role spans ML modeling, data pipelines, and backend systems (Go) to ensure models run reliably, efficiently, and at scale.

Responsibilities

• Build and optimize real-time data pipelines and backend services for device and behavioral data.
• Develop and deploy fraud detection ML models with production reliability and efficiency.
• Transform raw data into production-ready features for fraud detection.
• Collaborate with platform and backend engineers to integrate models seamlessly.
• Maintain security, privacy, and compliance standards.
• Drive testing, documentation, and observability best practices.

Requirements

• 5+ years in software engineering with strong backend experience (Go or Python).
• Hands-on applied ML experience with large datasets (e.g., PyTorch, scikit-learn).
• 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 English communication (written and verbal).
• BS or MS in Computer Science, Engineering, or related field.

Bonus / Nice to have

• Domain knowledge in fraud, risk, or cybersecurity.
• Familiarity with CI/CD, Docker, Kubernetes, and modern DevOps practices.
• Understanding of modern browser APIs and high-entropy data collection.
• Experience leveraging frontier LLMs for automation.

Location / work setup

• Remote-first: United States or Canada, work from home.