Machine Learning Engineer

Machine Learning Engineer

Build and own production-grade machine learning systems for real-time fraud detection—from data pipelines and feature engineering through deployment, monitoring, and backend integration.

Responsibilities

• Build and optimize real-time data pipelines and backend services processing device and behavioral data
• Develop, deploy, and maintain fraud-detection ML models in production
• Design scalable feature engineering pipelines from raw data
• Collaborate with backend/platform teams to integrate ML models into production systems
• Monitor model performance, detect drift, and continuously improve accuracy
• Uphold security, privacy, reliability, and compliance standards
• Apply best practices for testing, documentation, and observability
• Contribute to scalable backend services using Go and Python

Requirements

• BS or MS in Computer Science/Engineering (or related)
• 5–8 years software engineering experience with strong backend and applied ML
• End-to-end ML experience:
  • Feature engineering pipelines
  • Model deployment
  • Monitoring and drift detection
  • Continuous model improvement
• Strong backend programming in Go or Python
• Experience with latency-sensitive ML systems for real-time predictions
• Strong SQL skills; experience with relational and NoSQL databases
• Excellent written/verbal communication in English
• Ability to work independently in a fast-paced remote environment

Preferred Qualifications

• Experience in fraud detection / risk / cybersecurity / bot detection (e.g., device fingerprinting, VPN/proxy detection)
• Experience with Docker, Kubernetes, CI/CD, and modern DevOps practices
• Familiarity with browser APIs and high-entropy data collection
• Experience using frontier LLMs to automate engineering workflows
• Strong backend engineering background beyond traditional data science

Compensation & Location

• Fully remote (US or Canada)
• Salary: $175,000–$220,000 per year plus competitive equity