Machine Learning Engineer

Location: Remote (United States or Canada)
Employment Type: Full-time
Salary: $175,000–$220,000 per year
Hiring: 3 Open Positions

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'll own end-to-end ML solutions, from data pipelines and feature engineering to model deployment, monitoring, and backend infrastructure.

If you're passionate about building scalable ML systems and solving complex fraud detection challenges, we'd love to hear from you.

Key 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.

Required 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 Qualifications

• 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.

Tech Stack

• Go
• Python
• SQL
• Docker
• Kubernetes
• PyTorch
• Scikit-learn

Compensation & Benefits

• Salary: $175,000–$220,000 per year
• Competitive equity package
• 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

Work Authorization

• Visa sponsorship is not available.
• TN visa candidates are welcome to apply.
• L1 and O1 transfers may be considered on a case-by-case basis.
• H1-B sponsorship is not available.

Who Should Apply

We're looking for engineers who have successfully built and deployed machine learning systems in production—not candidates focused solely on research, experimentation, or ML Ops. Ideal applicants have experience delivering real-time ML solutions at scale and are comfortable owning projects from data pipeline development through deployment and ongoing optimization.

Apply today to help build the next generation of intelligent fraud detection systems.