Machine Learning Engineer
Design and implement machine learning models, data pipelines, and backend systems that power fraud detection in production. The role combines applied ML with large-scale systems engineering to deliver reliable, efficient, end-to-end solutions.
Responsibilities
- Build and optimize real-time data pipelines and backend services for device and behavioral data.
- Develop and deploy reliable, efficient ML models for fraud detection in production.
- Create production-ready features from raw data for fraud detection systems.
- Collaborate with platform and backend engineers to integrate models.
- Maintain security, privacy, and compliance standards.
- Champion testing, documentation, and observability best practices.
Requirements
- Hands-on experience with applied ML using large datasets, including PyTorch or 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 written and verbal English communication skills.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline.
Benefits
- Generous cash compensation and equity.
- Early exercise for all options, including pre-vested options.
- Remote-first work culture.
- Flexible paid time off and year-end break.
- Health, dental, and vision insurance for employees and dependents in the US and Canada.
- 4% 401k or RRSP matching in the US and Canada.
- MacBook Pro delivered to your door.
- One-time home-office setup stipend.
- Monthly meal and social meet-up stipends.
- Annual health, wellness, and learning stipends.