Machine Learning Engineer

About the role

As a Machine Learning Engineer, you’ll do more than build models - you’ll design the systems that make fraud detection possible. You’ll work across modeling, data pipelines, and backend systems (Go) to ensure ML models run reliably, efficiently, and at scale. This is a chance to combine applied ML with large-scale systems engineering, owning end-to-end solutions that tackle high-stakes, ever-evolving challenges.

Responsibilities

• Build and optimize data pipelines and backend services to process device and behavioral data in real time.
• Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production.
• Turn raw data into production-ready features that feed our fraud detection systems.
• Collaborate with platform and backend engineers to integrate models seamlessly.
• Maintain high standards of security, privacy, and compliance.
• Champion best practices in testing, documentation, and observability.

Requirements

• 5+ years in software engineering, with strong backend experience (Go or Python).
• Hands-on experience with applied ML using large datasets (PyTorch, Scikit-learn, etc.).
• 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 communication skills in English, both written and verbal.
• Bachelor's or Master's in Computer Science, Engineering, or a related discipline.

Qualifications

• Domain knowledge in fraud, risk, or cybersecurity (bonus point).
• Familiarity with CI/CD, Docker, Kubernetes and the modern devops framework (bonus point).
• Understanding of modern browser APIs and high-entropy data collection techniques (bonus point).
• Familiarity with leveraging frontier LLMs for automation (bonus point).

Skills

• Software engineering, backend experience (Go or Python).
• Applied ML using large datasets (PyTorch, Scikit-learn, etc.).
• SQL skills and database familiarity.
• End-to-end ML systems experience.
• Communication skills in English.
• Computer Science, Engineering, or related discipline degree.

Benefits

• Generous compensation in cash and equity.
• Early exercise for all options, including pre-vested.
• Work from anywhere: Remote-first Culture.
• Flexible paid time off and Year-end break.
• Health insurance, dental, and vision coverage for employees and dependents - US and Canada specific.
• 4% matching in 401k / RRSP - US and Canada specific.
• MacBook Pro delivered to your door.
• One-time stipend to set up a home office — desk, chair, screen, etc.
• Monthly meal stipend.
• Monthly social meet-up stipend.
• Annual health and wellness stipend.
• Annual Learning stipend.