Machine Learning Engineer

About the Role

We are seeking an experienced Machine Learning Engineer to design and build production-grade machine learning systems that power real-time fraud detection. This role goes beyond model development—you will own end-to-end ML solutions, from data pipelines and feature engineering to model deployment, monitoring, and backend infrastructure.

Responsibilities

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

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 5–8 years of software engineering experience with strong backend development and applied machine learning.

• Experience building and deploying end-to-end ML systems, including:
• Feature engineering pipelines
• Model deployment
• Monitoring and drift detection
• Continuous model improvement

• Strong backend programming experience in Go or Python.
• Experience building latency-sensitive ML systems serving real-time predictions.
• Strong SQL skills and experience working with relational and NoSQL databases.
• Excellent written and verbal English communication skills.
• Ability to work independently in a fast-paced, remote environment.

Preferred Skills

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

Pay

Pay range and compensation package: Salary: $175,000–$220,000 per year
Competitive equity package

Benefits

• Fully remote (US or Canada)
• Opportunity to work on cutting-edge fraud detection technology
• Collaborative engineering culture with significant ownership and impact
• Remote Work Policy: Remote-first within the United States and Canada. Office locations available in: Bay Area, New York City, Austin, Toronto, São Paulo

Hiring Details

• 3 Open Positions
• Apply today to help build the next generation of intelligent fraud detection systems.